import numpy as np
import pandas as pd
import re, string
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', None)
pd.set_option('max_colwidth', None)
def read_data(file, info=True):
try:
df = pd.read_csv(file)
if info:
print('\n', "="*10+"LOADING DATA"+"="*10)
print("Data reading is complete!")
print('\n', "="*10+"DIMENSIONS"+"="*10)
print("Number of Observations = ", df.shape[0])
print("Number of Columns = ", df.shape[1],'\n')
print('\n', "="*10+"DTYPES"+"="*10)
if len(df.select_dtypes("object").columns) > 0:
print('Number of Object variables = ',
len(df.select_dtypes("object").columns))
if len(df.select_dtypes("integer").columns) > 0:
print('Number of Integer variables = ',
len(df.select_dtypes("integer").columns))
if len(df.select_dtypes("float").columns) > 0:
print('Number of Float variables = ',
len(df.select_dtypes("float").columns))
if len(df.select_dtypes("bool").columns) > 0:
print('Number of Boolean variables = ',
len(df.select_dtypes("bool").columns))
print('\n', "="*10+"MISSING VALUES"+"="*10)
print(np.where(df.isnull().values.any() == False,
"No missing value! Proceed to next steps..."
, "Data includes missing value! Analyze missing values"))
return df
except:
print('Data did not import!')
df = read_data('case_study_data.csv',info=True)
df.head(2)
==========LOADING DATA========== Data reading is complete! ==========DIMENSIONS========== Number of Observations = 268343 Number of Columns = 3 ==========DTYPES========== Number of Object variables = 2 Number of Integer variables = 1 ==========MISSING VALUES========== No missing value! Proceed to next steps...
| complaint_id | product_group | text | |
|---|---|---|---|
| 0 | 2815595 | bank_service | On XX/XX/2017 my check # XXXX was debited from my checking account, and this check wasn't cashed by XXXX XXXX XXXX, I requested a copy of the endorsement of whom the fund was credited to, Bank of America can't give me satisfactory answer, since this contractor breach of contract that I needed proof of payment in court. |
| 1 | 2217937 | bank_service | I opened a Bank of the the West account. The account came with a promotion. The promotion was get {$100.00} for setting up a qualifying direct deposit. I have received that promotion. The other promotion was, " get {$50.00} for making XXXX debit card purchases each month for 2 months. '' I have met the terms for the {$50.00} promotion and have not received the promotion that I am entitled to. I want the {$50.00} that I am entitled to that Bank of the West has not given me. |
def plot_counts(df, var, axs):
chart = sns.countplot(x=df[var],order = df[var].value_counts().index,ax=axs)
chart.set_xticklabels(chart.get_xticklabels(),rotation=90)
for p, label in zip(axs.patches, df[var].value_counts()):
axs.annotate(label, (p.get_x()+0.1, p.get_height()+1));
fig, axs =plt.subplots(1,2, figsize=(15,5))
fig.suptitle('Distribution of Product Groups', weight='bold', size=16)
plot_counts(df, 'product_group', axs[0])
df.groupby('product_group')['product_group'].count().plot.pie(
startangle=90, autopct="%.1f%%", ax=axs[1]);
df_nlp = df.copy()
# Create Features
df_nlp['length'] = df_nlp['text'].apply(len)
df_nlp['num_words'] = df_nlp['text'].str.split(' ').apply(len)
df_nlp['masked_XX'] = df_nlp['text'].str.split(' ').apply(lambda xx: sum(1 for w in xx if 'XX' in w.upper()))
df_nlp['caps_letter'] = df_nlp['text'].apply(lambda capsL: sum(1 for c in capsL if c.isupper()))
df_nlp['caps_word'] = df_nlp['text'].apply(lambda capsW : sum(1 for c in capsW.split() if c.isupper()))
df_nlp['exclaimations'] = df_nlp['text'].apply(lambda exclaim : exclaim.count('!'))
df_nlp['questions'] = df_nlp['text'].apply(lambda questions : questions.count('?'))
df_nlp['punct'] = df_nlp['text'].apply(lambda punct: sum(punct.count(p) for p in string.punctuation))
df_nlp['sp_char'] = df_nlp['text'].apply(lambda sp_char: sum(sp_char.count(s) for s in '@#$%^*()-'))
df_nlp['unique_words'] = df_nlp['text'].apply(lambda text: len(set(w for w in text.split())))
# Manipulate to find ratios
df_nlp['capsL_to_length'] = df_nlp['caps_letter'] / df_nlp['length']
df_nlp['capsW_to_length'] = df_nlp['caps_word'] / df_nlp['length']
df_nlp['capsW_to_words'] = df_nlp['caps_word'] / df_nlp['num_words']
df_nlp['unique_to_words'] = df_nlp['unique_words']/df_nlp['num_words']
df_nlp['masked_to_words'] = df_nlp['masked_XX']/df_nlp['num_words']
df_nlp.describe(exclude=['O'], percentiles=[0.05,0.10,0.25,0.50,0.75,0.90,0.95,0.99])
| complaint_id | length | num_words | masked_XX | caps_letter | caps_word | exclaimations | questions | punct | sp_char | unique_words | capsL_to_length | capsW_to_length | capsW_to_words | unique_to_words | masked_to_words | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2.683430e+05 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 | 268343.000000 |
| mean | 2.306798e+06 | 1081.071628 | 199.005765 | 10.281013 | 85.082339 | 21.716624 | 0.311743 | 0.216506 | 28.086352 | 3.774952 | 104.923538 | 0.082369 | 0.021188 | 0.114700 | 0.637097 | 0.052961 |
| std | 4.910311e+05 | 1149.470828 | 210.259267 | 19.972965 | 168.185135 | 37.327132 | 2.178775 | 1.248002 | 40.067409 | 10.433873 | 78.188712 | 0.109624 | 0.024142 | 0.132965 | 0.206245 | 0.065825 |
| min | 1.290155e+06 | 5.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.013021 | 0.000000 |
| 5% | 1.418536e+06 | 131.000000 | 24.000000 | 0.000000 | 4.000000 | 1.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 20.000000 | 0.014973 | 0.003654 | 0.020576 | 0.411658 | 0.000000 |
| 10% | 1.539736e+06 | 203.000000 | 37.000000 | 0.000000 | 7.000000 | 2.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 30.000000 | 0.020833 | 0.006462 | 0.035915 | 0.453757 | 0.000000 |
| 25% | 1.907122e+06 | 392.000000 | 72.000000 | 2.000000 | 19.000000 | 6.000000 | 0.000000 | 0.000000 | 8.000000 | 0.000000 | 51.000000 | 0.034586 | 0.010909 | 0.059880 | 0.530211 | 0.014085 |
| 50% | 2.413685e+06 | 746.000000 | 138.000000 | 5.000000 | 45.000000 | 13.000000 | 0.000000 | 0.000000 | 17.000000 | 1.000000 | 86.000000 | 0.056027 | 0.016279 | 0.088000 | 0.626374 | 0.036364 |
| 75% | 2.738736e+06 | 1372.000000 | 254.000000 | 12.000000 | 95.000000 | 26.000000 | 0.000000 | 0.000000 | 35.000000 | 4.000000 | 137.000000 | 0.088246 | 0.023280 | 0.125000 | 0.734694 | 0.068966 |
| 90% | 2.872482e+06 | 2363.000000 | 435.000000 | 24.000000 | 186.000000 | 47.000000 | 1.000000 | 1.000000 | 64.000000 | 10.000000 | 205.000000 | 0.140065 | 0.033939 | 0.181818 | 0.841270 | 0.115672 |
| 95% | 2.919234e+06 | 3236.900000 | 594.000000 | 36.000000 | 277.000000 | 68.000000 | 2.000000 | 1.000000 | 89.000000 | 15.000000 | 258.000000 | 0.207716 | 0.047288 | 0.253731 | 0.909091 | 0.159664 |
| 99% | 2.967017e+06 | 4757.000000 | 876.580000 | 77.000000 | 651.000000 | 145.000000 | 6.000000 | 4.000000 | 159.000000 | 33.000000 | 348.000000 | 0.781250 | 0.170940 | 0.965274 | 1.000000 | 0.330660 |
| max | 2.995824e+06 | 31735.000000 | 6320.000000 | 1811.000000 | 24835.000000 | 5644.000000 | 368.000000 | 342.000000 | 5229.000000 | 1518.000000 | 1839.000000 | 0.933333 | 0.260000 | 10.000000 | 74.000000 | 1.000000 |
df_nlp.describe(exclude=['O']).T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| complaint_id | 268343.0 | 2.306798e+06 | 491031.135984 | 1.290155e+06 | 1.907122e+06 | 2.413685e+06 | 2.738736e+06 | 2.995824e+06 |
| length | 268343.0 | 1.081072e+03 | 1149.470828 | 5.000000e+00 | 3.920000e+02 | 7.460000e+02 | 1.372000e+03 | 3.173500e+04 |
| num_words | 268343.0 | 1.990058e+02 | 210.259267 | 1.000000e+00 | 7.200000e+01 | 1.380000e+02 | 2.540000e+02 | 6.320000e+03 |
| masked_XX | 268343.0 | 1.028101e+01 | 19.972965 | 0.000000e+00 | 2.000000e+00 | 5.000000e+00 | 1.200000e+01 | 1.811000e+03 |
| caps_letter | 268343.0 | 8.508234e+01 | 168.185135 | 0.000000e+00 | 1.900000e+01 | 4.500000e+01 | 9.500000e+01 | 2.483500e+04 |
| caps_word | 268343.0 | 2.171662e+01 | 37.327132 | 0.000000e+00 | 6.000000e+00 | 1.300000e+01 | 2.600000e+01 | 5.644000e+03 |
| exclaimations | 268343.0 | 3.117428e-01 | 2.178775 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 3.680000e+02 |
| questions | 268343.0 | 2.165065e-01 | 1.248002 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 0.000000e+00 | 3.420000e+02 |
| punct | 268343.0 | 2.808635e+01 | 40.067409 | 0.000000e+00 | 8.000000e+00 | 1.700000e+01 | 3.500000e+01 | 5.229000e+03 |
| sp_char | 268343.0 | 3.774952e+00 | 10.433873 | 0.000000e+00 | 0.000000e+00 | 1.000000e+00 | 4.000000e+00 | 1.518000e+03 |
| unique_words | 268343.0 | 1.049235e+02 | 78.188712 | 1.000000e+00 | 5.100000e+01 | 8.600000e+01 | 1.370000e+02 | 1.839000e+03 |
| capsL_to_length | 268343.0 | 8.236949e-02 | 0.109624 | 0.000000e+00 | 3.458647e-02 | 5.602716e-02 | 8.824587e-02 | 9.333333e-01 |
| capsW_to_length | 268343.0 | 2.118846e-02 | 0.024142 | 0.000000e+00 | 1.090909e-02 | 1.627907e-02 | 2.327988e-02 | 2.600000e-01 |
| capsW_to_words | 268343.0 | 1.147000e-01 | 0.132965 | 0.000000e+00 | 5.988024e-02 | 8.800000e-02 | 1.250000e-01 | 1.000000e+01 |
| unique_to_words | 268343.0 | 6.370967e-01 | 0.206245 | 1.302083e-02 | 5.302106e-01 | 6.263736e-01 | 7.346939e-01 | 7.400000e+01 |
| masked_to_words | 268343.0 | 5.296059e-02 | 0.065825 | 0.000000e+00 | 1.408451e-02 | 3.636364e-02 | 6.896552e-02 | 1.000000e+00 |
def explore_features(feature, threshold):
df_feature = df_nlp.loc[df_nlp[feature] > threshold]
bold_start = '\033[1m'
bold_end = '\033[0m'
print(bold_start+ "{} : ".format(feature) + bold_end +
"{} Percentage of records are with more than {} {} in train data and the count is {}".format(
round(len(df_feature)/len(df_nlp)*100,2), threshold, feature, len(df_feature)))
# Using manually selected thresholds for CUSTOM FEATURES
for (i,j) in zip(['sp_char', 'exclaimations', 'questions', 'punct', 'masked_XX'],[15,5,5,75, 25]):
explore_features(i, j)
sp_char : 4.8 Percentage of records are with more than 15 sp_char in train data and the count is 12890 exclaimations : 1.19 Percentage of records are with more than 5 exclaimations in train data and the count is 3194 questions : 0.56 Percentage of records are with more than 5 questions in train data and the count is 1495 punct : 7.22 Percentage of records are with more than 75 punct in train data and the count is 19370 masked_XX : 9.27 Percentage of records are with more than 25 masked_XX in train data and the count is 24872
# Using manually selected thresholds for BASIC FEATURES
for i,j in zip(['length', 'num_words', 'caps_word', 'unique_words'],[3500, 600, 70, 300]):
explore_features(i, j)
length : 4.03 Percentage of records are with more than 3500 length in train data and the count is 10810 num_words : 4.86 Percentage of records are with more than 600 num_words in train data and the count is 13041 caps_word : 4.58 Percentage of records are with more than 70 caps_word in train data and the count is 12282 unique_words : 2.36 Percentage of records are with more than 300 unique_words in train data and the count is 6331
# Using manually selected thresholds for DERIVED RATIO FEATURES
for i,j in zip(['unique_to_words','capsL_to_length','capsW_to_words', 'masked_to_words'],
[ 0.90, 0.20,0.25,0.15]):
explore_features(i, j)
unique_to_words : 5.39 Percentage of records are with more than 0.9 unique_to_words in train data and the count is 14473 capsL_to_length : 5.29 Percentage of records are with more than 0.2 capsL_to_length in train data and the count is 14185 capsW_to_words : 5.07 Percentage of records are with more than 0.25 capsW_to_words in train data and the count is 13609 masked_to_words : 5.71 Percentage of records are with more than 0.15 masked_to_words in train data and the count is 15314
import warnings
warnings.filterwarnings('ignore')
def visualize_features(feature, threshold, X='product_group',):
# Filter any extreme values
_df = df_nlp[df_nlp[feature] <= threshold]
f, axs = plt.subplots(1,2,figsize=(20,6))
f.suptitle("Distribution of " + feature, weight='bold', size=16)
my_palette = {"bank_service": "tab:blue", "credit_card": "tab:orange", "credit_reporting":"tab:green",
"debt_collection":"tab:red", "loan":"tab:purple" ,"money_transfers":"tab:brown" ,
"mortgage":"tab:pink"
}
# Create Boxplot
chart_ = sns.boxplot(x = X, y = feature, data = _df, ax=axs[0], palette=my_palette);
chart_.set_xticklabels(chart_.get_xticklabels(),rotation=30)
# Create Density plot
for (i,j) in zip(df_nlp.product_group.unique(),my_palette.keys()):
var = _df[_df.product_group == i][feature]
sns.distplot(var, hist=False, label=i, ax=axs[1], fit_kws={"color":my_palette[j]})
axs[1].legend()
plt.show()
for (i,j) in zip(['sp_char', 'exclaimations', 'questions', 'punct', 'masked_XX'],[15,5,5,75,25]):
visualize_features(i, j)
"""
sp_char : No variation among different product groups - No information contained
exclaimations : too few to be valuable
questions : too few to be valuable
punct : distribution of punctuations varies across groups - will keep the counts, but remove in TEXT PROCESSING
masked_XX : 'Mortgage' has higest median and 'Debt_collection' has lowest median - varies across groups
- will keep the counts, but remove in TEXT PROCESSING
"""
for i,j in zip(['length', 'num_words', 'caps_word', 'unique_words'],[3500, 600, 70, 300]):
visualize_features(i, j)
"""
All of these features varies across groups. However, these features may have high correlation
Next step : Check correlation and discard features
"""
# dropping 'QUESTIONS' and 'EXCLAIMATIONS' as they are too few to be useful
# dropping 'SP_CHAR' as there is very little variation / information
correlation_matrix = df_nlp.drop(columns=['complaint_id','questions','exclaimations','sp_char']).corr()
mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))
f, ax = plt.subplots(figsize=(20, 10))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
# Draw the heatmap with the mask and correct aspect ratio
sns.heatmap(correlation_matrix, mask=mask, cmap=cmap, center=0,
square=True, linewidths=.5,
vmin=-1, vmax=1, annot=True);
"""
1. "Length", "num_words" and "unique_words" - turned out to be most information in ditribution
2. "capsW_to_words" have very small correlation with "Length", "num_words" and "unique_words"
3. Same is true for "masked_to_words"
4. But "unique_to_words" shows higher correlation
5. Also, as expected "Length" is highly correlated with "num_words" and "unique_words"
, as "num_words" is highly correlated with "unique_words" as well
6. massed_xx and masked_to_words are also potential features
Conclusion:
Although these features look interesting, we will now move to NLP
as the focus of the project is to analyze text data
"""
# for next steps we will only keep few columns
df_text = df.copy()
from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import TruncatedSVD
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_selection import chi2
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
import nltk
from nltk import sent_tokenize,word_tokenize
from nltk.corpus import stopwords
from tqdm import tqdm
from nltk.stem.porter import *
from nltk.stem import WordNetLemmatizer
import gensim
"""
First, we will explore the corpus before preprocessing or cleaning
This will give a broader idea of the underlying data
We will explore explore most and least frequent data
We already saw in EDA the punctuation or special characters are not useful,
therefore will remove in text cleaning stage
"""
total_corpus = [i.split() for i in df_text['text'].values]
len(total_corpus)
268343
total_corpus_word = [w for sublist in total_corpus for w in sublist]
unique_corpus_word = list(set(total_corpus_word))
print("total words = ", len(total_corpus_word), "total unique words = ", len(unique_corpus_word))
total words = 52848489 total unique words = 285139
df_text['length'] = df_text['text'].str.split(' ').apply(len)
print('Mean number of words in document = ',df_text['length'].mean())
Mean number of words in document = 199.00576500970772
"""
Now we will look at most frequent words in corpus
"""
from sklearn.feature_extraction.text import CountVectorizer
def create_ngrams(text, ngram, top_n_words):
text = text.apply(lambda x: np.str_(x))
cv = CountVectorizer(ngram_range=(ngram, ngram)).fit(text)
bag_of_words = cv.transform(text)
sum_of_words = bag_of_words.sum(axis=0)
words_freq = [(w, sum_of_words[0, i]) for w, i in cv.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)
return words_freq[:top_n_words], dict(words_freq[:top_n_words])
_, unigram = create_ngrams(df_text['text'],1,50)
_, bigram = create_ngrams(df_text['text'],2,50)
_, trigram = create_ngrams(df_text['text'],3,50)
fig, axs = plt.subplots(1,3,figsize = (25,10))
fig.suptitle("Most frequent 50 ngrams", weight='bold', size=20)
sns.barplot(x=list(unigram.values()),y=list(unigram.keys()),ax=axs[0]); axs[0].set_title('Monogram')
sns.barplot(x=list(bigram.values()),y=list(bigram.keys()),ax=axs[1]); axs[1].set_title('Bigram')
sns.barplot(x=list(trigram.values()),y=list(trigram.keys()),ax=axs[2]); axs[2].set_title('Trigram');
def most_freq_word(df, n=10, show_top_n=True):
_, unigram = create_ngrams(df['text'],1,n)
_, bigram = create_ngrams(df['text'],2,n)
_, trigram = create_ngrams(df['text'],3,n)
if show_top_n:
fig, axs = plt.subplots(1,3,figsize = (25,10))
fig.suptitle("Most frequent %d ngrams - " %n + i, weight='bold', size=20)
sns.barplot(x=list(unigram.values()),y=list(unigram.keys()),ax=axs[0]); axs[0].set_title('Monogram')
sns.barplot(x=list(bigram.values()),y=list(bigram.keys()),ax=axs[1]); axs[1].set_title('Bigram')
sns.barplot(x=list(trigram.values()),y=list(trigram.keys()),ax=axs[2]); axs[2].set_title('Trigram');
return unigram, bigram, trigram
for i in df_text.product_group.unique():
_df = df_text.loc[df_text.product_group == i]
_,_,_ = most_freq_word(_df)
most_freq_monogram = {}
most_freq_bigram = {}
most_freq_trigram = {}
for i in df_text.product_group.unique():
_df = df_text.loc[df_text.product_group == i]
m,b,t = most_freq_word(_df, n=1000, show_top_n=False)
m_word = list(m.keys())
b_word = list(b.keys())
t_word = list(t.keys())
most_freq_monogram[i] = m_word
most_freq_bigram[i] = b_word
most_freq_trigram[i] = t_word
most_freq_monogram = pd.DataFrame(most_freq_monogram)
most_freq_bigram = pd.DataFrame(most_freq_bigram)
most_freq_trigram = pd.DataFrame(most_freq_trigram)
most_freq_monogram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | the | the | xxxx | xxxx | xxxx | xxxx | xxxx |
| 1 | xxxx | xxxx | the | the | the | the | the |
| 2 | to | to | to | to | to | to | to |
| 3 | and | and | and | and | and | and | and |
| 4 | my | my | my | my | my | my | my |
most_freq_bigram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx |
| 1 | my account | xx xx | xx xx | xx xx | xx xx | xx xx | xx xx |
| 2 | xx xx | xx xxxx | my credit | my credit | xx xxxx | xx xxxx | xx xxxx |
| 3 | xx xxxx | credit card | xx xxxx | xx xxxx | of the | my account | of the |
| 4 | of the | my credit | credit report | of the | xxxx and | the money | xxxx and |
most_freq_trigram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx |
| 1 | xx xx xxxx | xx xx xxxx | xx xx xxxx | xx xx xxxx | xx xx xxxx | xx xx xxxx | xx xx xxxx |
| 2 | on xxxx xxxx | on xx xx | my credit report | my credit report | on xx xx | on xx xx | on xxxx xxxx |
| 3 | on xx xx | on xxxx xxxx | on my credit | on my credit | on xxxx xxxx | on xxxx xxxx | on xx xx |
| 4 | bank of america | my credit card | xxxx xx xx | on xx xx | in xx xx | xxxx xxxx and | in xx xx |
"""
Next - will explore least frequent ngrams in corpus
"""
def create_ngrams2(text, ngram, top_n_words):
text = text.apply(lambda x: np.str_(x))
cv = CountVectorizer(ngram_range=(ngram, ngram)).fit(text)
bag_of_words = cv.transform(text)
sum_of_words = bag_of_words.sum(axis=0)
words_freq = [(w, sum_of_words[0, i]) for w, i in cv.vocabulary_.items()]
words_freq =sorted(words_freq, key = lambda x: x[1])
return words_freq[:top_n_words], dict(words_freq[:top_n_words])
_, unigram = create_ngrams2(df_text['text'],1,50)
_, bigram = create_ngrams2(df_text['text'],2,50)
_, trigram = create_ngrams2(df_text['text'],3,50)
fig, axs = plt.subplots(1,3,figsize = (25,10))
fig.suptitle("Least frequent 50 ngrams", weight='bold', size=20)
sns.barplot(x=list(unigram.values()),y=list(unigram.keys()),ax=axs[0]); axs[0].set_title('Monogram')
sns.barplot(x=list(bigram.values()),y=list(bigram.keys()),ax=axs[1]); axs[1].set_title('Bigram')
sns.barplot(x=list(trigram.values()),y=list(trigram.keys()),ax=axs[2]); axs[2].set_title('Trigram');
def least_freq_word(df, n=10, show_top_n=True):
_, unigram = create_ngrams2(df['text'],1,n)
_, bigram = create_ngrams2(df['text'],2,n)
_, trigram = create_ngrams2(df['text'],3,n)
if show_top_n:
fig, axs = plt.subplots(1,3,figsize = (25,10))
fig.suptitle("Least frequent %d ngrams - " %n + i, weight='bold', size=20)
sns.barplot(x=list(unigram.values()),y=list(unigram.keys()),ax=axs[0]); axs[0].set_title('Monogram')
sns.barplot(x=list(bigram.values()),y=list(bigram.keys()),ax=axs[1]); axs[1].set_title('Bigram')
sns.barplot(x=list(trigram.values()),y=list(trigram.keys()),ax=axs[2]); axs[2].set_title('Trigram');
return unigram, bigram, trigram
for i in df_text.product_group.unique():
_df = df_text.loc[df_text.product_group == i]
_,_,_ = least_freq_word(_df)
l_freq_monogram = {}
l_freq_bigram = {}
l_freq_trigram = {}
for i in df_text.product_group.unique():
_df = df_text.loc[df_text.product_group == i]
m,b,t = least_freq_word(_df, n=1000, show_top_n=False)
m_word = list(m.keys())
b_word = list(b.keys())
t_word = list(t.keys())
l_freq_monogram[i] = m_word
l_freq_bigram[i] = b_word
l_freq_trigram[i] = t_word
l_freq_monogram = pd.DataFrame(l_freq_monogram)
l_freq_bigram = pd.DataFrame(l_freq_bigram)
l_freq_trigram = pd.DataFrame(l_freq_trigram)
l_freq_monogram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | custimers | supiscious | l2inc | contraire | dibilitating | recounted | 2321 |
| 1 | ec | juiy | spooing | xxxx9 | ques | becase | hma |
| 2 | ivite | resurfacing | inally | perfecly | originat | unexpired | discord |
| 3 | dividened | ureasonable | clc | passcodes | autodrafts | dtd | shiny |
| 4 | mustve | takeoff | dangers | remides | eps | iao | escrowthe |
l_freq_bigram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | wasn cashed | since however | three inaccurate | settle 4000 | suspension since | main concern | on box |
| 1 | contractor breach | some 1500 | month 840 | official my | creditor 17 | shows messages | npv property |
| 2 | purchases each | year computer | correctly requested | take asked | joining military | messages not | qualify see |
| 3 | nj opened | 00 turned | giving response | evasive in | on orders | transfer pending | attached actual |
| 4 | early 2015 | renew off | updated noting | correspondence re | orders assigning | pending am | actual appeal |
l_freq_trigram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | this check wasn | paid them as | disputed three inaccurate | financial systems agreed | exactly sure who | com for buying | modification hamp xxxx |
| 1 | check wasn cashed | agreed monthly ever | three inaccurate 30 | to settle 4000 | time xxxx or | selling bitcoin my | mistake on box |
| 2 | wasn cashed by | ever since however | to experian submitted | settle 4000 00 | xxxx or sallie | bitcoin my main | on box xxxx |
| 3 | of the endorsement | since however now | experian including record | 4000 00 debt | mae my first | my main concern | my npv property |
| 4 | endorsement of whom | me credit any | each month 840 | 2000 00 if | first disbursement date | main concern is | npv property taxes |
"""
At this stage we will clean the text by pre-processing
We observed the punctuations, including 'Question marks' and ' Exclaimations' are not useful in EDA
Also, the stop words are appearing everywhere and overcrowding the corpus without adding any valuable information
Next, we will apply lemmatization. Although Stemming could be applied, we chose Lemmatization in this step
The following function 'pre_preprocess_text' will help us just do all these steps
"""
def pre_preprocess_text(text, stemming=False, lemmatizing=True, sw=None, punct_list = None):
## clean (convert to lowercase and remove punctuations and characters and then strip)
text = re.sub(r'[^\w\s]', '', str(text).lower().strip())
## Tokenize (convert from string to list)
text_token = text.split()
## remove Stopwords
if sw is not None:
text_stopwordFree = [word for word in text_token if word not in sw]
# Checking all words is alphabets or not - if not remove
text_alphabets = [i for i in text_stopwordFree if i.isalpha()]
# punctuations free
if punct_list is not None:
text_punctFree = [w for w in text_alphabets if w not in punct_list]
## Stemming (remove -ing, -ly, ...)
if stemming == True:
ps = nltk.stem.porter.PorterStemmer()
text_clean = [ps.stem(word) for word in text_punctFree]
## Lemmatisation (convert the word into root word)
if lemmatizing == True:
lem = nltk.stem.wordnet.WordNetLemmatizer()
text_clean = [lem.lemmatize(word) for word in text_punctFree]
## back to string from list
text = " ".join(text_clean)
return text
stopwords_list = nltk.corpus.stopwords.words("english")
puncts = [i for i in string.punctuation]
df_text['clean_text'] = df_text['text'].apply(lambda x: pre_preprocess_text(x, sw=stopwords_list,
punct_list = puncts))
"""
After text processing, we want to revisit most frequent words in corpus and in each product groups
"""
_, unigram = create_ngrams(df_text['clean_text'],1,50)
_, bigram = create_ngrams(df_text['clean_text'],2,50)
_, trigram = create_ngrams(df_text['clean_text'],3,50)
fig, axs = plt.subplots(1,3,figsize = (25,10))
fig.suptitle("Most frequent 50 ngrams", weight='bold', size=20)
sns.barplot(x=list(unigram.values()),y=list(unigram.keys()),ax=axs[0]); axs[0].set_title('Monogram')
sns.barplot(x=list(bigram.values()),y=list(bigram.keys()),ax=axs[1]); axs[1].set_title('Bigram')
sns.barplot(x=list(trigram.values()),y=list(trigram.keys()),ax=axs[2]); axs[2].set_title('Trigram');
def most_freq_word(df, n=10, show_top_n=True):
_, unigram = create_ngrams(df['clean_text'],1,n)
_, bigram = create_ngrams(df['clean_text'],2,n)
_, trigram = create_ngrams(df['clean_text'],3,n)
if show_top_n:
fig, axs = plt.subplots(1,3,figsize = (25,10))
fig.suptitle("Most frequent %d ngrams - " %n + i, weight='bold', size=20)
sns.barplot(x=list(unigram.values()),y=list(unigram.keys()),ax=axs[0]); axs[0].set_title('Monogram')
sns.barplot(x=list(bigram.values()),y=list(bigram.keys()),ax=axs[1]); axs[1].set_title('Bigram')
sns.barplot(x=list(trigram.values()),y=list(trigram.keys()),ax=axs[2]); axs[2].set_title('Trigram');
return unigram, bigram, trigram
for i in df_text.product_group.unique():
_df = df_text.loc[df_text.product_group == i]
_,_,_ = most_freq_word(_df)
most_freq_monogram = {}
most_freq_bigram = {}
most_freq_trigram = {}
for i in df_text.product_group.unique():
_df = df_text.loc[df_text.product_group == i]
m,b,t = most_freq_word(_df, n=1000, show_top_n=False)
m_word = list(m.keys())
b_word = list(b.keys())
t_word = list(t.keys())
most_freq_monogram[i] = m_word
most_freq_bigram[i] = b_word
most_freq_trigram[i] = t_word
most_freq_monogram = pd.DataFrame(most_freq_monogram)
most_freq_bigram = pd.DataFrame(most_freq_bigram)
most_freq_trigram = pd.DataFrame(most_freq_trigram)
most_freq_monogram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | xxxx | xxxx | xxxx | xxxx | xxxx | xxxx | xxxx |
| 1 | account | card | credit | debt | loan | account | payment |
| 2 | bank | credit | account | credit | payment | money | loan |
| 3 | check | account | report | account | would | bank | mortgage |
| 4 | would | payment | xxxxxxxx | collection | xxxxxxxx | xxxxxxxx | xxxxxxxx |
most_freq_bigram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx | xxxx xxxx |
| 1 | checking account | credit card | credit report | credit report | student loan | bank account | well fargo |
| 2 | well fargo | capital one | credit bureau | collection agency | credit report | western union | loan modification |
| 3 | bank america | customer service | xxxxxxxx xxxx | debt collection | interest rate | wire transfer | mortgage payment |
| 4 | debit card | american express | xxxx xxxxxxxx | credit bureau | loan xxxx | well fargo | bank america |
most_freq_trigram.head()
| bank_service | credit_card | credit_reporting | debt_collection | loan | money_transfers | mortgage | |
|---|---|---|---|---|---|---|---|
| 0 | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx | xxxx xxxx xxxx |
| 1 | account xxxx xxxx | credit card company | xxxxxxxx xxxx xxxx | debt collection practice | loan xxxx xxxx | account xxxx xxxx | xxxx xxxx received |
| 2 | bank xxxx xxxx | credit card account | xxxx xxxx xxxxxxxx | fair debt collection | private student loan | xxxx xxxx account | loan xxxx xxxx |
| 3 | well fargo bank | xxxx credit card | xxxx xxxxxxxx xxxx | collection practice act | payment xxxx xxxx | bank xxxx xxxx | xxxxxxxx xxxx xxxx |
| 4 | xxxx xxxx account | xxxxxxxx xxxx xxxx | xxxx xxxx account | xxxxxxxx xxxx xxxx | xxxx xxxx xxxxxxxx | xxxxxxxx xxxx xxxx | payment xxxx xxxx |
for i,j in zip(['masked_XX', 'masked_to_words'],
[ 15,0.15]):
explore_features(i, j)
masked_XX : 18.79 Percentage of records are with more than 15 masked_XX in train data and the count is 50432 masked_to_words : 5.71 Percentage of records are with more than 0.15 masked_to_words in train data and the count is 15314
for (i,j) in zip(['masked_XX','masked_to_words'],[15,0.15]):
visualize_features(i, j)
"""
1. We can clearly see masking is overcrwoding the text data - but also saw there is variations in number
of masked words across different groups - Therefore, decided to keep them
2. We used Bag of Words (BoW) to get most and least frequent words
3. Now, finally we will build TF-IDF - which will be used in the model development
"""
# PCA - using TF-IDF
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
tfv = TfidfVectorizer(min_df=3, max_features=1000,
strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}',
ngram_range=(1, 1), use_idf=1,smooth_idf=1,sublinear_tf=1,
stop_words = 'english')
text_pca = df_text['clean_text']
tfv.fit(text_pca)
text_pca_tfv = tfv.transform(text_pca)
X = text_pca_tfv.todense()
reduced_data = PCA(n_components=2).fit_transform(X)
my_palette = {"bank_service": "tab:blue", "credit_card": "tab:orange", "credit_reporting":"tab:green",
"debt_collection":"tab:red", "loan":"tab:purple" ,"money_transfers":"tab:brown" ,
"mortgage":"tab:pink"
}
clss = df_text['product_group'].values.reshape(len(df_text),1)
X_pca = pd.DataFrame(np.hstack((reduced_data,clss)), columns = ['PCA1', 'PCA2','class'])
# this was with 1000 features
X_pca['color'] = X_pca['class'].map(my_palette)
plt.figure(figsize = (12,6))
sns.scatterplot(data=X_pca, x='PCA1', y='PCA2', hue='class');
X_pca.head()
| PCA1 | PCA2 | class | color | |
|---|---|---|---|---|
| 0 | -0.058879 | -0.052114 | bank_service | tab:blue |
| 1 | -0.084443 | -0.109562 | bank_service | tab:blue |
| 2 | -0.049013 | -0.051678 | bank_service | tab:blue |
| 3 | -0.1633 | -0.104556 | bank_service | tab:blue |
| 4 | -0.082661 | -0.064461 | bank_service | tab:blue |
"""
The PCA analysis shows the 2 dimensional projection of high dimension TF-IFD vectors
The PCA results suggests that it will be easier to idenfy few product groups, whereas in some
product groups, finding the correct classes may be difficult
Overall the plot is promosing and we can expect to generate satisfying results
"""
def run_TFIDF(corpus, custom_stopwords=True, vocabs = None):
if custom_stopwords:
sw = set(to_remove)
else:
sw = 'english'
if vocabs == None:
print ("Creating TF-IDF based on MonoGram and BiGram only due to computation limitations...")
print ("Limiting maximum features to 10k in TF-IDF only due to computation limitations...")
tfv = TfidfVectorizer(min_df=3, max_features=10000,
strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}',
ngram_range=(1, 2), use_idf=1,smooth_idf=1,sublinear_tf=1,
stop_words = sw)
else:
print ("Creating TF-IDF based on MonoGram and BiGram only due to computation limitations...")
print ("Limiting maximum features to %d in TF-IDF only due to computation limitations..." %len(vocabs))
v_list = vocabs
tfv = TfidfVectorizer(min_df=3, max_features=10000,
strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}',
ngram_range=(1, 2), use_idf=1,smooth_idf=1,sublinear_tf=1,
stop_words = sw, vocabulary = v_list)
print("fitting TF-IDF ...")
tfv.fit(corpus)
print("transforming TF-IDF ...")
text_tfv = tfv.transform(corpus)
dic_tfv = tfv.vocabulary_
return dic_tfv, tfv, text_tfv
def get_most_predictive_features(df, top_n, custom_sw=True, show_top_5=True):
corpus = df['clean_text']
dic_tfv, tfv, text_tfv = run_TFIDF(corpus, custom_sw)
print('Converting from Sparse to Dense...')
features = text_tfv.toarray()
print('Convertion to Dense complete...')
top_n_features = []
for product in df_text.product_group.unique():
features_chi2 = chi2(features, df_text["product_group"] == product)
indices = np.argsort(features_chi2[0])
feature_names = np.array(tfv.get_feature_names())[indices]
unigrams = [v for v in feature_names if len(v.split(' ')) == 1]
bigrams = [v for v in feature_names if len(v.split(' ')) == 2]
top_n_features.append(unigrams[-top_n:])
if show_top_5:
print("n==> %s:" %(product))
print(" * Most Correlated Unigrams are: %s" %(', '.join(unigrams[-5:])))
print(" * Most Correlated Bigrams are: %s" %(', '.join(bigrams[-5:])))
return dic_tfv, tfv, text_tfv, top_n_features
"""
Implemented Chi-Square test to identify most predictive co-efficients to classify the text data
We also printed out top 5 most useful 'unigrams' and 'bigrams' for each product groups
Finally, these also helped us reducing the number of most important features from 7000 to 2541
For next steps, these 2541 most informative unigrams will be used for model building
"""
dic_tfv, tfv, text_tfv, top_n_each = get_most_predictive_features(df_text, 1000, custom_sw=False)
top_n_each_flat = [w for sublist in top_n_each for w in sublist]
top_n_uniques = list(set(top_n_each_flat))
print(len(top_n_each_flat), "==>",len(top_n_uniques))
Creating TF-IDF based on MonoGram and BiGram only due to computation limitations... Limiting maximum features to 10k in TF-IDF only due to computation limitations... fitting TF-IDF ... transforming TF-IDF ... Converting from Sparse to Dense... Convertion to Dense complete... n==> bank_service: * Most Correlated Unigrams are: branch, checking, bank, deposit, overdraft * Most Correlated Bigrams are: direct deposit, debit card, saving account, overdraft fee, checking account n==> credit_card: * Most Correlated Unigrams are: amex, citi, express, reward, card * Most Correlated Bigrams are: card company, balance transfer, annual fee, american express, credit card n==> credit_reporting: * Most Correlated Unigrams are: transunion, report, experian, inquiry, equifax * Most Correlated Bigrams are: xxxx equifax, equifax xxxx, credit file, hard inquiry, credit report n==> debt_collection: * Most Correlated Unigrams are: collector, recovery, collect, collection, debt * Most Correlated Bigrams are: trying collect, debt collector, debt collection, collect debt, collection agency n==> loan: * Most Correlated Unigrams are: school, repayment, student, loan, navient * Most Correlated Bigrams are: repayment plan, loan forgiveness, pay loan, private loan, student loan n==> money_transfers: * Most Correlated Unigrams are: bitcoin, wire, paypal, western, coinbase * Most Correlated Bigrams are: sent money, money transfer, wire transfer, coinbase account, western union n==> mortgage: * Most Correlated Unigrams are: property, foreclosure, escrow, modification, mortgage * Most Correlated Bigrams are: short sale, escrow account, mortgage company, mortgage payment, loan modification 7000 ==> 2541
dic_tfv, tfv, text_tfv, top_n_each = get_most_predictive_features(df_text, 1000, custom_sw=False)
top_n_each_flat = [w for sublist in top_n_each for w in sublist]
top_n_uniques = list(set(top_n_each_flat))
print(len(top_n_each_flat), "==>",len(top_n_uniques))
Creating TF-IDF based on MonoGram and BiGram only due to computation limitations... Limiting maximum features to 10k in TF-IDF only due to computation limitations... fitting TF-IDF ... transforming TF-IDF ... Converting from Sparse to Dense... Convertion to Dense complete... n==> bank_service: * Most Correlated Unigrams are: branch, checking, bank, deposit, overdraft * Most Correlated Bigrams are: direct deposit, debit card, saving account, overdraft fee, checking account n==> credit_card: * Most Correlated Unigrams are: amex, citi, express, reward, card * Most Correlated Bigrams are: card company, balance transfer, annual fee, american express, credit card n==> credit_reporting: * Most Correlated Unigrams are: transunion, report, experian, inquiry, equifax * Most Correlated Bigrams are: xxxx equifax, equifax xxxx, credit file, hard inquiry, credit report n==> debt_collection: * Most Correlated Unigrams are: collector, recovery, collect, collection, debt * Most Correlated Bigrams are: trying collect, debt collector, debt collection, collect debt, collection agency n==> loan: * Most Correlated Unigrams are: school, repayment, student, loan, navient * Most Correlated Bigrams are: repayment plan, loan forgiveness, pay loan, private loan, student loan n==> money_transfers: * Most Correlated Unigrams are: bitcoin, wire, paypal, western, coinbase * Most Correlated Bigrams are: sent money, money transfer, wire transfer, coinbase account, western union n==> mortgage: * Most Correlated Unigrams are: property, foreclosure, escrow, modification, mortgage * Most Correlated Bigrams are: short sale, escrow account, mortgage company, mortgage payment, loan modification 7000 ==> 2541
# sample 100 words and see correlation heatmap
sns.heatmap(text_tfv.todense()[:,np.random.randint(0,1000,100)]==0,
vmin=0, vmax=1, cbar=False).set_title('Sparse Matrix Sample');
tfidf_df = pd.DataFrame(text_tfv.toarray(),columns=tfv.get_feature_names())
tfidf_df_sample.head()
| aadvantage | abandoned | abide | ability | ability obtain | ability pay | able | able access | able afford | able contact | able help | able make | able obtain | able pay | able provide | able reach | able speak | able use | able verify | able work | absence | absolute | absolutely | absurd | abuse | abused | abusive | ac | acc | acc xxxx | accept | accept payment | acceptable | acceptance | accepted | accepted payment | accepting | access | access account | access credit | access fund | access information | access money | access online | access xxxx | accessed | accessing | accident | accidentally | accordance | accordance fair | accordance fcra | according | according credit | according fair | according fcra | according xxxx | accordingly | account | account able | account access | account according | account account | account accurate | account active | account activity | account added | account address | account advised | account applied | account asked | account attached | account authorized | account balance | account bank | account believe | account belong | account belongs | account business | account called | account capital | account card | account changed | account charge | account charged | account chase | account check | account checking | account citibank | account claim | account close | account closed | account closure | account collection | account company | account contact | account contacted | account continue | account correct | account cover | account credit | account credited | account creditor | account current | account currently | account customer | account date | account day | account debt | account delete | account deleted | account delinquent | account deposit | account different | account dispute | account disputed | account ending | account equifax | account error | account experian | account explained | account fact | account fargo | account fee | account file | account filed | account fraud | account fraudulent | account frozen | account fund | account gave | account given | account going | account good | account got | account help | account history | account hold | account holder | account immediately | account inaccurate | account included | account including | account info | account information | account informed | account inquiry | account instead | account issue | account know | account knowledge | account late | account letter | account like | account linked | account listed | account loan | account locked | account longer | account make | account making | account manager | account money | account month | account monthly | account mortgage | account multiple | account need | account negative | account new | account notice | account noticed | account nt | account number | account old | account online | account open | account opened | account opening | account order | account original | account overdraft | account overdrawn | account paid | account past | account pay | account payment | account permission | account personal | account phone | account placed | account point | account previously | account prior | account provide | account provided | account question | account reason | account receive | account received | account recently | account record | account refuse | account refused | account remain | account remove | account removed | account report | account reported | account reporting | account representative | account request | account requested | account result | account review | account said | account saw | account say | account section | account send | account sent | account service | account set | account settled | account showed | account showing | account sold | account spoke | account state | account stated | account statement | account status | account taken | account time | account today | account told | account took | account transaction | account transfer | account transferred | account transunion | account tried | account type | account u | account unable | account updated | account use | account used | account using | account valid | account verified | account want | account way | account week | account went | account xxxx | account xxxxxxxx | account xxxxxxxxxxxx | account year | accountability | accountable | accountant | accounting | accrue | accrued | accruing | acct | acct xxxx | accts | accumulated | accuracy | accuracy account | accuracy completeness | accuracy information | accurate | accurate credit | accurate information | accurately | accusation | accused | accusing | ace | ach | acknowledge | acknowledged | acknowledgement | acknowledging | acquire | acquired | acquisition | act | act fair | act fcra | act fdcpa | act including | act section | act usc | act violation | act xxxx | acted | acting | action | action company | action lawsuit | action taken | action xxxx | activate | activated | active | actively | activity | activity account | activity cease | activity xxxx | actual | actually | ad | ad astra | add | added | added account | added credit | added loan | added xxxx | addendum | adding | addition | addition xxxx | additional | additional charge | additional document | additional fee | additional fund | additional information | additional money | additional month | additional payment | additional principal | additional xxxx | additionally | address | address account | address address | address associated | address concern | address credit | address file | address information | address issue | address listed | address lived | address nt | address original | address phone | address provided | address received | address request | address send | address sent | address social | address telephone | address xxxx | address year | addressed | addressing | adequate | adequately | adhere | adjust | adjustable | adjustable rate | adjusted | adjuster | adjustment | administration | administrative | administrator | admission | admit | admitted | admitting | adult | advance | advanced | advantage | adverse | adverse action | adversely | advertised | advertisement | advertising | advice | advise | advised | advised account | advised contact | advised u | advised xxxx | advising | advisor | advocate | aes | affair | affect | affect credit | affected | affected credit | affecting | affecting credit | affidavit | affiliate | affiliated | afford | afford pay | afford payment | affordable | afforded | afni | aforementioned | afraid | afternoon | age | age xxxx | agency | agency account | agency called | agency collection | agency contacted | agency credit | agency debt | agency information | agency received | agency remove | agency removed | agency report | agency reported | agency reporting | agency sent | agency shall | agency told | agency xxxx | agency xxxxxxxx | agent | agent said | agent told | agent xxxx | aggressive | ago | ago received | ago told | ago xxxx | agree | agreed | agreed pay | agreed payment | agreed remove | agreed xxxx | agreeing | agreement | agreement pay | agreement signed | agreement xxxx | agrees | ahead | ahold | aid | air | airline | aka | aka xxxx | al | alabama | alert | alert account | alert credit | alert placed | alert xxxx | alerted | allegation | allege | alleged | alleged account | alleged debt | alleged debtor | allegedly | alleging | allied | allocated | allotted | allow | allow u | allowed | allowed xxxx | allowing | allows | ally | ally bank | ally financial | alot | altered | alternative | amazon | amended | amendment | america | america account | america credit | america xxxx | american | american education | american express | amerihome | amex | amortization | amortization schedule | ample | amt | analysis | andor | angry | announced | annoying | annual | annual credit | annual fee | annually | answer | answer phone | answer question | answer xxxx | answered | answered phone | answering | answering machine | anticipated | anybody | anymore | anytime | anyways | apart | apartment | apartment complex | apartment xxxx | apologize | apologized | apology | app | appalled | apparent | apparently | appeal | appealed | appear | appear court | appear credit | appear report | appearance | appeared | appeared credit | appearing | appearing credit | appears | appears credit | appears xxxx | appliance | applicable | applicant | application | application credit | application denied | application loan | application process | application submitted | application xxxx | applied | applied account | applied card | applied credit | applied home | applied loan | applied modification | applied mortgage | applied payment | applied principal | applied xxxx | applies | apply | apply credit | apply loan | apply payment | apply xxxx | applying | applying credit | applying loan | applying payment | appointment | appraisal | appraisal fee | appraisal xxxx | appraised | appraised value | appraiser | appreciate | appreciate help | appreciated | approach | approached | appropriate | appropriately | approval | approval letter | approve | approved | approved credit | approved loan | approved xxxx | approving | approx | approx xxxx | approximately | approximately month | approximately xxxx | approximately year | apr | ar | arbitrarily | arbitrary | arbitration | area | area code | arent | argue | argued | arguing | argument | arizona | arm | arrange | arranged | arrangement | arrangement pay | arrears | arrest | arrested | arrive | arrived | article | asap | aside | ask | ask help | ask question | ask xxxx | asked | asked account | asked bank | asked company | asked contact | asked copy | asked credit | asked email | asked help | asked information | asked loan | asked make | asked manager | asked nt | asked pay | asked payment | asked proof | asked provide | asked question | asked remove | asked repeatedly | asked representative | asked said | asked send | asked speak | asked stop | asked supervisor | asked time | asked told | asked u | asked validation | asked verification | asked verify | asked wanted | asked xxxx | asking | asking help | asking information | asking pay | asking payment | asking question | asking remove | asking xxxx | asks | aspect | ass | assertion | assessed | assessment | asset | asset management | assign | assigned | assigned xxxx | assignee | assignment | assignment mortgage | assist | assist getting | assistance | assistance matter | assistance program | assistance xxxx | assistant | assisted | assisting | associate | associate account | associate xxxx | associated | associated account | association | assume | assumed | assuming | assumption | assurance | assure | assured | astra | atm | atm card | atm machine | attach | attached | attached account | attached complaint | attached copy | attached credit | attached document | attached documentation | attached email | attached letter | attached proof | attached xxxx | attaching | attaching copy | attachment | attachment xxxx | attack | attempt | attempt collect | attempt contact | attempt correct | attempt resolve | attempt xxxx | attempted | attempted collect | attempted contact | attempted dispute | attempted make | attempted resolve | attempting | attempting collect | attend | attended | attended xxxx | attending | attention | attention matter | attitude | attorney | attorney fee | attorney general | attorney office | attorney xxxx | atty | auction | audit | aunt | authority | authorization | authorization form | authorize | authorize inquiry | authorize xxxx | authorized | authorized inquiry | authorized user | authorized xxxx | authorizing | auto | auto finance | auto financing | auto insurance | auto loan | auto pay | auto payment | automated | automatic | automatic payment | automatically | automobile | automotive | automotive xxxx | autopay | avail | availability | available | available balance | available credit | available xxxx | avant | avenue | average | avoid | avoid foreclosure | avoided | avoiding | awaiting | award | awarded | aware | aware debt | aware xxxx | away | away xxxx | awful | az | az xxxx | b | b xxxx | ba | baby | backed | background | background check | bad | bad check | bad credit | bad debt | bad faith | badly | bag | bait | bait switch | bal | balance | balance account | balance balance | balance called | balance card | balance charged | balance credit | balance date | balance day | balance loan | balance month | balance monthly | balance nt | balance owed | balance paid | balance past | balance pay | balance payment | balance received | balance reported | balance time | balance told | balance transfer | balance xxxx | balance xxxxxxxx | ball | balloon | balloon payment | bank | bank account | bank america | bank asked | bank bank | bank branch | bank called | bank card | bank charge | bank charged | bank check | bank checking | bank claim | bank closed | bank credit | bank customer | bank day | bank employee | bank error | bank fee | bank fraud | bank home | bank information | bank informed | bank loan | bank make | bank manager | bank money | bank mortgage | bank na | bank need | bank nt | bank paid | bank pay | bank payment | bank received | bank record | bank refuse | bank refused | bank representative | bank said | bank say | bank sent | bank stated | bank statement | bank stating | bank time | bank told | bank took | bank want | bank west | bank xxxx | bank xxxxxxxx | bank year | banker | banker xxxx | banking | banking account | banking information | banking institution | bankrupt | bankruptcy | bankruptcy attorney | bankruptcy case | bankruptcy chapter | bankruptcy court | bankruptcy credit | bankruptcy discharged | bankruptcy filed | bankruptcy filing | bankruptcy xxxx | bankruptcy xxxxxxxx | bar | barclay | barclaycard | barclays | barclays bank | barely | barred | base | based | based income | based information | based repayment | based xxxx | basic | basically | basis | battle | bayview | bayview loan | bb | bbb | bbva | bbva compass | bc | bear | bearing | bearing signature | bed | bedroom | began | began receiving | began xxxx | begging | begin | beginning | beginning xxxx | beginning xxxxxxxx | begun | behalf | behalf xxxx | behavior | belief | believe | believe company | believe payment | believe xxxx | believed | believing | belong | belong xxxx | belonged | belonging | belongs | beneficial | beneficiary | benefit | best | best buy | best regard | better | better business | better rate | bid | big | big picture | biggest | billed | billing | billing cycle | billing dispute | billing error | billing statement | binding | birth | birth certificate | birth date | birthdate | birthday | bit | bitcoin | biweekly | biweekly payment | bk | black | blame | blamed | blank | blatant | blatantly | block | block information | block requested | blocked | blocking | blue | bmo | bmo harris | bmw | boa | board | body | bofa | bogus | bond | bonded | bonus | bonus mile | bonus offer | bonus point | bonus xxxx | book | booked | born | borrow | borrow money | borrowed | borrower | borrowing | bos | bother | bothered | bought | bought car | bought home | bought house | bought xxxx | bounce | bounced | bound | box | boyfriend | bpo | branch | branch manager | branch xxxx | brand | brand new | breach | breach contract | breach detected | breach little | breach million | breached | break | breakdown | breaking | breaking law | breech | brief | bring | bring account | bringing | broke | broken | broker | brother | brought | brought attention | bsi | budget | build | build credit | builder | building | built | bullied | bully | bullying | bunch | burden | bureau | bureau account | bureau cfpb | bureau collection | bureau company | bureau credit | bureau creditor | bureau day | bureau dispute | bureau equifax | bureau late | bureau remove | bureau removed | bureau report | bureau reporting | bureau sent | bureau time | bureau told | bureau xxxx | bureau xxxxxxxx | business | business account | business address | business bureau | business card | business checking | business company | business credit | business day | business hour | business practice | business xxxx | busy | button | buy | buy car | buy credit | buy home | buy house | buyer | buying | buying home | buying house | c | ca | ca nt | ca xxxx | cable | calculate | calculated | calculation | calendar | calendar day | caliber | caliber home | california | california xxxx | called | called american | called ask | called asked | called bank | called called | called capital | called cell | called chase | called check | called citi | called citibank | called collection | called company | called credit | called customer | called day | called emailed | called equifax | called experian | called explained | called fargo | called got | called immediately | called informed | called inquire | called left | called make | called mortgage | called multiple | called navient | called number | called numerous | called office | called phone | called said | called sent | called spoke | called stated | called talked | called tell | called time | called today | called told | called transunion | called twice | called u | called week | called work | called xxxx | called xxxxxxxx | caller | caller id | calling | calling cell | calling number | calling time | calling work | calling xxxx | came | came home | came line | came said | came verified | came xxxx | camera | cancel | cancel account | cancel card | canceled | canceled check | canceling | cancellation | cancelled | cancelled card | cancelled check | cancelled xxxx | cancelling | cap | capacity | capital | capital account | capital auto | capital bank | capital credit | capital xxxx | capitalized | capitalone | capitol | car | car accident | car dealership | car insurance | car loan | car payment | car repossessed | car xxxx | card | card account | card application | card asked | card balance | card bank | card called | card cancelled | card capital | card card | card charge | card charged | card chase | card closed | card company | card credit | card customer | card day | card debt | card declined | card ending | card fraud | card got | card holder | card information | card issued | card issuer | card limit | card mail | card make | card member | card month | card new | card nt | card number | card offer | card online | card opened | card paid | card pay | card payment | card purchase | card received | card said | card sent | card service | card statement | card stolen | card time | card told | card transaction | card use | card used | card xxxx | card xxxxxxxx | card year | cardholder | cardmember | care | care credit | care xxxx | career | careful | carefully | carolina | carpet | carried | carrier | carrington | carrington mortgage | carry | carrying | case | case closed | case manager | case number | case xxxx | cash | cash advance | cash check | cash deposit | cash reward | cash xxxx | cashed | cashed check | cashed xxxx | cashier | cashier check | cashing | catch | category | caught | cause | caused | caused credit | caused xxxx | causing | cavalry | cbe | cc | cc xxxx | cd | cease | cease collection | cease desist | ceased | cell | cell number | cell phone | cellphone | cent | center | center xxxx | central | ceo | ceo xxxx | certain | certainly | certificate | certification | certified | certified copy | certified letter | certified mail | cfpb | cfpb case | cfpb complaint | cfpb help | cfpb xxxx | cfpb xxxxxxxx | cfr | ch | ch xxxx | chain | challenge | challenged | challenging | chance | change | change account | change address | change credit | change date | change payment | change term | change xxxx | changed | changed xxxx | changing | channel | chapter | chapter xxxx | character | charge | charge account | charge added | charge balance | charge bank | charge called | charge card | charge charge | charge credit | charge fee | charge late | charge month | charge nt | charge paid | charge removed | charge reversed | charge time | charge told | charge u | charge xxxx | charge xxxxxxxx | chargeback | charged | charged account | charged card | charged credit | charged fee | charged late | charged month | charged overdraft | charged u | charged xxxx | charged xxxxxxxx | chargedoff | chargeoff | charging | charging fee | charging late | charging xxxx | chase | chase account | chase bank | chase branch | chase card | chase credit | chase customer | chase mortgage | chase representative | chase told | chase xxxx | chat | cheated | check | check account | check balance | check bank | check called | check cashed | check check | check cleared | check credit | check day | check deposit | check deposited | check fee | check fraud | check issued | check mail | check mailed | check nt | check number | check paid | check pay | check payment | check received | check returned | check said | check sent | check status | check told | check written | check xxxx | check xxxxxxxx | checked | checked account | checked bank | checked credit | checked xxxx | checking | checking account | checking credit | checking saving | chexsystems | child | child support | child xxxx | chip | choice | choose | choosing | chose | chosen | chrysler | chrysler capital | circle | circuit | circumstance | cited | citi | citi bank | citi card | citi mortgage | citi xxxx | citibank | citibank credit | citibank xxxx | citicard | citigold | citigold checking | citimortgage | citing | citizen | citizen bank | city | city xxxx | civil | civil liability | claim | claim account | claim action | claim court | claim credit | claim debt | claim deed | claim denied | claim department | claim disputed | claim filed | claim number | claim owe | claim received | claim sent | claim xxxx | claimant | claimed | claimed owed | claiming | claiming owe | clarification | clarify | clarity | class | class action | clause | clean | cleaning | clear | clear violation | clearance | cleared | cleared bank | cleared xxxx | clearing | clearly | clearly state | clearly stated | clerk | click | clicked | client | client xxxx | clock | close | close account | close loan | close xxxx | closed | closed account | closed balance | closed bank | closed case | closed credit | closed date | closed paid | closed xxxx | closed xxxxxxxx | closed year | closely | closer | closing | closing account | closing cost | closing date | closing disclosure | closing document | closing xxxx | closure | club | clue | coast | code | code section | code xxxx | coin | coinbase | coinbase account | collateral | collect | collect alleged | collect debt | collect money | collect payment | collect state | collect xxxx | collected | collecting | collecting debt | collection | collection account | collection act | collection action | collection activity | collection agency | collection agent | collection attempt | collection bureau | collection company | collection credit | collection debt | collection department | collection effort | collection item | collection letter | collection notice | collection paid | collection practice | collection proceeding | collection removed | collection report | collection reported | collection service | collection xxxx | collection xxxxxxxx | collector | collector xxxx | college | college xxxx | colorado | combined | come | come xxxx | comenity | comenity bank | comfortable | coming | comment | commerce | commercial | commission | commit | commitment | committed | committed fraud | committing | committing fraud | common | commonwealth | commonwealth financial | communicate | communicated | communicating | communication | communication xxxx | community | comp | company | company able | company account | company allowed | company asked | company business | company called | company calling | company claim | company company | company contact | company contacted | company continues | company credit | company day | company debt | company failed | company file | company help | company information | company informed | company know | company letter | company like | company listed | company loan | company make | company money | company month | company named | company need | company nt | company paid | company pay | company phone | company policy | company provide | company provided | company received | company refuse | company refused | company remove | company report | company reported | company reporting | company represent | company representative | company requested | company responded | company response | company said | company say | company send | company sent | company state | company stated | company stating | company stop | company time | company told | company took | company tried | company trying | company violated | company want | company work | company xxxx | company xxxxxxxx | company year | compare | compared | compass | compass bank | compensate | compensated | compensation | competent | competent evidence | complain | complainant | complained | complaining | complaint | complaint bank | complaint cfpb | complaint company | complaint consumer | complaint credit | complaint equifax | complaint experian | complaint fargo | complaint filed | complaint ftc | complaint number | complaint regarding | complaint submitted | complaint transunion | complaint xxxx | complaint xxxxxxxx | complete | complete accurate | complete investigation | complete payment | complete xxxx | completed | completed xxxx | completed xxxxxxxx | completely | completely different | completely removed | completeness | completeness information | completing | completion | complex | compliance | compliant | complicated | complied | comply | comply federal | comply request | complying | compromise | compromised | computer | concern | concern xxxx | concerned | concerning | concerning account | concluded | conclusion | condescending | condition | conditional | condo | conduct | conduct business | conduct investigation | conducted | conducting | conference | confidence | confident | confidential | confirm | confirmation | confirmation email | confirmation number | confirmation xxxx | confirmed | confirmed account | confirmed xxxx | confirming | confirms | conflict | conflicting | conflicting information | confused | confusing | confusion | congress | conn | connect | connected | connection | consecutive | consent | consent order | consent xxxx | consented | consequence | consequently | consider | consideration | considered | considering | consistent | consistently | consolidate | consolidate loan | consolidated | consolidated loan | consolidation | consolidation loan | constant | constantly | constitute | constitutes | construction | construed | consult | consultant | consulted | consulting | consumer | consumer affair | consumer complaint | consumer consumer | consumer contract | consumer credit | consumer debt | consumer dispute | consumer file | consumer finance | consumer financial | consumer information | consumer protection | consumer report | consumer reporting | consumer request | consumer right | consumer statement | consumer usa | consumer xxxx | contact | contact attorney | contact bank | contact collection | contact company | contact credit | contact creditor | contact customer | contact info | contact information | contact number | contact person | contact phone | contact regarding | contact u | contact xxxx | contacted | contacted attorney | contacted bank | contacted capital | contacted chase | contacted citi | contacted collection | contacted company | contacted credit | contacted creditor | contacted customer | contacted debt | contacted equifax | contacted experian | contacted fargo | contacted mortgage | contacted navient | contacted phone | contacted regarding | contacted time | contacted told | contacted transunion | contacted u | contacted xxxx | contacted xxxxxxxx | contacting | contacting xxxx | contain | contained | containing | contains | content | continually | continue | continue harass | continue make | continue pay | continue receive | continue report | continue send | continued | continued make | continued pay | continued report | continues | continues report | continuing | continuing collection | continuing report | continuous | continuously | contract | contract account | contract agreement | contract company | contract signature | contract signed | contract xxxx | contracted | contractor | contractual | contractual obligation | contrary | control | convenience | convenient | conventional | conventional loan | convergent | convergent outsourcing | conversation | conversation xxxx | conversion | converted | convinced | cooper | cooperate | cooperation | copay | copied | copy | copy bank | copy check | copy contract | copy credit | copy document | copy documentation | copy driver | copy email | copy identity | copy judgment | copy letter | copy original | copy police | copy receipt | copy report | copy signature | copy signed | copy social | copy statement | copy verifiable | copy xxxx | corp | corporate | corporate office | corporation | correct | correct account | correct address | correct credit | correct date | correct error | correct information | correct issue | correct mistake | correct problem | correct record | correct xxxx | corrected | corrected credit | corrected xxxx | correcting | correction | correctly | correspond | correspondence | correspondence sent | correspondence xxxx | corresponding | corrupt | cosign | cosigned | cosigner | cost | cost xxxx | costco | costing | counsel | counseling | counselor | count | counted | counter | counting | countless | countless hour | country | countrywide | county | county clerk | county court | county tax | county xxxx | couple | couple day | couple month | couple time | couple week | couple year | coupon | course | court | court case | court date | court document | court house | court law | court order | court record | court xxxx | courtesy | courthouse | cover | coverage | covered | covering | coworker | cr | cra | cra violation | cras | crazy | create | created | creates | creating | credit | credit acceptance | credit account | credit act | credit agency | credit application | credit applied | credit balance | credit bank | credit bureau | credit called | credit card | credit check | credit collection | credit company | credit credit | credit day | credit debt | credit department | credit dispute | credit file | credit fraud | credit freeze | credit good | credit grantor | credit history | credit information | credit inquires | credit inquiry | credit issue | credit late | credit limit | credit line | credit loan | credit management | credit monitoring | credit month | credit need | credit nt | credit paid | credit payment | credit profile | credit protection | credit pull | credit pulled | credit rating | credit received | credit record | credit repair | credit report | credit reporting | credit ruined | credit score | credit sent | credit service | credit time | credit told | credit union | credit worthiness | credit xxxx | credit xxxxxxxx | credit year | credited | credited account | credited xxxx | crediting | creditor | creditor account | creditor collection | creditor credit | creditor debt | creditor xxxx | crime | criminal | crisis | criterion | critical | crook | csr | cst | ct | cure | currency | current | current account | current address | current balance | current credit | current loan | current mortgage | current payment | current status | current xxxx | currently | currently reporting | currently xxxx | customer | customer account | customer bank | customer care | customer representative | customer service | customer support | customer xxxx | customer year | cut | cycle | dad | daily | daily basis | damage | damage credit | damaged | damaged credit | damaging | damaging credit | danger | dark | data | data breach | data furnisher | database | date | date account | date activity | date balance | date birth | date called | date credit | date date | date day | date debt | date delinquency | date filed | date inquiry | date letter | date loan | date month | date open | date opened | date paid | date payment | date receipt | date received | date reported | date request | date service | date set | date time | date xxxx | date xxxxxxxx | date xxxxxxxxxxxx | dated | dated xxxx | dated xxxxxxxx | dated xxxxxxxxxxxx | dating | daughter | daughter xxxx | day | day account | day ago | day asked | day bank | day called | day check | day closing | day complete | day completion | day contacted | day credit | day date | day day | day delinquent | day dispute | day got | day grace | day investigate | day late | day later | day letter | day loan | day make | day mark | day money | day month | day notice | day nt | day paid | day passed | day past | day pay | day payment | day period | day prior | day process | day receipt | day receive | day received | day receiving | day respond | day response | day said | day sent | day time | day today | day told | day week | day went | day work | day xxxx | day xxxxxxxx | dba | dc | dead | deadline | deal | deal xxxx | dealer | dealer service | dealer xxxx | dealership | dealership xxxx | dealing | dealing xxxx | dealt | dear | dear cfpb | dear sir | dear xxxx | death | death certificate | debit | debit account | debit card | debit xxxx | debited | debited account | debt | debt account | debt agreement | debt asked | debt belong | debt belongs | debt buyer | debt called | debt claim | debt collection | debt collector | debt company | debt contacted | debt credit | debt creditor | debt date | debt day | debt debt | debt discharged | debt dispute | debt disputed | debt income | debt information | debt know | debt letter | debt listed | debt need | debt nt | debt original | debt owe | debt owed | debt paid | debt pay | debt provide | debt provided | debt received | debt recovery | debt removed | debt report | debt reported | debt reporting | debt request | debt requested | debt result | debt said | debt say | debt sent | debt settled | debt signed | debt sold | debt state | debt time | debt told | debt trying | debt valid | debt validated | debt validation | debt verification | debt verified | debt want | debt xxxx | debt xxxxxxxx | debt year | debtor | decade | deceased | deceitful | deceive | deceived | deceiving | decent | deception | deceptive | deceptive practice | decide | decided | decided close | decision | declaration | declared | decline | declined | declining | decrease | decreased | decree | dedicated | deduct | deducted | deductible | deduction | deed | deed lieu | deed trust | deemed | deeply | defamation | defamation character | default | default judgment | default loan | default xxxx | defaulted | defaulted loan | defective | defend | defendant | defense | defer | deferment | deferral | deferred | deficiency | defined | definitely | definition | defraud | defrauded | defrauding | degree | delaware | delay | delayed | delaying | delete | delete account | delete credit | delete information | delete item | delete xxxx | deleted | deleted account | deleted credit | deleted immediately | deleted report | deleted xxxx | deleting | deletion | deliberate | deliberately | delinquency | delinquency credit | delinquency date | delinquency xxxx | delinquency xxxxxxxx | delinquent | delinquent account | delinquent credit | delinquent payment | delinquent xxxx | deliver | delivered | delivered xxxx | delivery | delta | demand | demand letter | demand payment | demanded | demanding | demanding payment | demonstrate | demonstrated | denial | denial letter | denied | denied access | denied claim | denied credit | denied loan | denied request | denied xxxx | denies | dental | dentist | deny | denying | department | department education | department said | department told | department xxxx | dependent | depending | deposit | deposit account | deposit box | deposit check | deposit money | deposit xxxx | deposited | deposited account | deposited check | deposited xxxx | depositing | depot | dept | dept education | dept xxxx | derogatory | derogatory account | derogatory information | derogatory mark | derogatory remark | described | description | description procedure | deserve | designated | designed | desire | desired | desist | desist letter | desist usc | desk | desperate | desperately | despite | despite fact | destroy | destroyed | destroying | detailed | detailing | detected | detected received | detective | determination | determine | determine accuracy | determined | determining | detrimental | development | device | diagnosed | diagnosed xxxx | didnt | didnt know | didnt receive | didnt want | died | difference | different | different account | different address | different answer | different bank | different collection | different company | different credit | different date | different department | different information | different loan | different number | different occasion | different people | different phone | different representative | different story | different time | different xxxx | differently | difficult | difficult time | difficulty | digit | digit social | digital | diligence | diligently | dime | direct | direct debit | direct deposit | direct express | direct violation | directed | direction | directly | directly xxxx | director | disabled | disagree | disappeared | disappointed | disappointed equifax | disaster | disbursed | disbursement | discharge | discharged | discharged bankruptcy | discharged xxxx | discharged xxxxxxxx | disclose | disclosed | disclosing | disclosure | disclosure statement | disconnect | disconnected | discontinue | discontinued | discount | discover | discover bank | discover card | discover credit | discovered | discovered xxxx | discovery | discrepancy | discretion | discriminated | discriminating | discrimination | discriminatory | discus | discus matter | discussed | discussing | discussion | disgusted | dishonest | dismiss | dismissal | dismissed | display | displayed | disposition | dispute | dispute account | dispute charge | dispute claim | dispute company | dispute credit | dispute day | dispute debt | dispute department | dispute equifax | dispute experian | dispute filed | dispute information | dispute inquiry | dispute item | dispute letter | dispute online | dispute process | dispute received | dispute request | dispute resolution | dispute resolved | dispute result | dispute sent | dispute told | dispute transunion | dispute validity | dispute xxxx | dispute xxxxxxxx | disputed | disputed account | disputed accuracy | disputed charge | disputed collection | disputed consumer | disputed credit | disputed debt | disputed gone | disputed information | disputed item | disputed resolve | disputed time | disputed xxxx | disputing | disputing account | disputing charge | disputing debt | disputing xxxx | disregard | disregarded | disrespectful | distress | distribution | district | district court | disturbing | ditech | ditech financial | diversified | diversified consultant | division | divorce | divorce decree | divorced | dmv | dob | dob xxxxxxxx | dob xxxxxxxxxxxx | doc | docket | doctor | doctor office | document | document attached | document company | document file | document needed | document proof | document prove | document provided | document received | document recorded | document requested | document sent | document showing | document signed | document submitted | document time | document told | document used | document xxxx | document xxxxxxxx | documentation | documentation file | documentation provided | documentation received | documentation requested | documentation sent | documentation showing | documentation support | documentation xxxx | documented | documenting | doddfrank | doesnt | dog | dollar | dollar month | dollar xxxx | dont | dont care | dont know | dont owe | dont understand | dont want | door | double | double payment | doubled | doubt | dovenmuehle | download | dozen | draft | draft fee | drafted | dragging | dramatically | drastically | draw | drawn | dream | drive | driven | driven repayment | driver | driver license | driving | drop | drop credit | drop xxxx | dropped | dropped credit | dropped point | dropped xxxx | dropping | drove | dual | dual tracking | dunning | duplicate | duplicated | duration | duress | duty | e | ear | earlier | earlier year | early | early termination | early warning | early xxxx | early xxxxxxxx | earn | earned | earnest | earnest money | earning | easier | easily | eastern | easy | eat | ebay | ecmc | economic | economy | ed | education | education service | education xxxx | effect | effect credit | effected | effecting | effective | effective date | effective xxxx | effective xxxxxxxx | effectively | effort | egregious | elderly | elected | electric | electronic | electronic payment | electronically | eligibility | eligible | eliminate | email account | email address | email confirmation | email fraudulent | email letter | email notification | email phone | email received | email response | email saying | email sent | email stating | email xxxx | email xxxxxxxx | email xxxxxxxxxxxx | emailed | emailed xxxx | emailing | embarrassed | embarrassing | emergency | emergency room | emotional | employed | employed xxxx | employee | employee id | employee xxxx | employer | employer xxxx | employment | enclosed | enclosed copy | encountered | encourage | encouraged | end | end day | end loan | end month | end xxxx | end xxxxxxxx | end year | ended | ended xxxx | ending | ending xxxx | endless | endorsed | endorsement | energy | enforce | enforce right | enforcement | enforcement agency | engage | engaged | engaging | engine | english | enhanced | enhanced recovery | enormous | enroll | enrolled | enrollment | ensure | enter | entered | entering | entire | entire balance | entire process | entire time | entirely | entirety | entitled | entity | entry | entry credit | envelope | eos | equal | equifax | equifax continues | equifax credit | equifax data | equifax equifax | equifax failed | equifax handled | equifax mishandled | equifax refuse | equifax remove | equifax report | equifax reporting | equifax security | equifax xxxx | equifax xxxxxxxx | equipment | equitable | equity | equity home | equity line | equity loan | equivalent | er | erc | erroneous | erroneous account | erroneous information | erroneously | error | error account | error corrected | error credit | error message | error reporting | error xxxx | escalate | escalated | escalating | escalation | escalation department | escrow | escrow account | escrow analysis | escrow balance | escrow payment | escrow shortage | escrow xxxx | especially | essence | essentially | est | establish | established | estate | estate agent | estate tax | estimate | estimated | et | et seq | ethic | ethical | evaluation | evening | event | eventually | everyday | everytime | evicted | eviction | evidence | evidence bearing | evidenced | evident | evidently | ex | ex husband | ex wife | exact | exactly | example | example xxxx | exceed | exceeded | exceeds | excellent | excellent credit | exception | excess | excessive | exchange | excuse | executed | execution | executive | executive office | executor | exempt | exemption | exercise | exeter | exhausted | exhibit | exhibit xxxx | exhusband | exist | existed | existence | existing | exists | exorbitant | expect | expectation | expected | expecting | expedite | expedited | expense | expensive | experian | experian credit | experian refuse | experian remove | experian report | experian reporting | experian xxxx | experience | experienced | experiencing | expert | expiration | expiration date | expire | expired | expires | expiring | explain | explain situation | explained | explained situation | explained xxxx | explaining | explaining situation | explains | explanation | explicitly | exposed | express | express card | express xxxx | expressed | ext | ext xxxx | extend | extended | extending | extension | extension xxxx | extensive | extent | external | extort | extortion | extra | extra money | extra payment | extreme | extremely | extremely disappointed | extremely rude | exwife | eye | f | fabricated | face | faced | facility | facing | fact | fact account | fact paid | fact received | fact xxxx | factor | factual | factual record | fail | fail comply | failed | failed comply | failed notify | failed pay | failed properly | failed provide | failed remove | failed respond | failed send | failed validate | failing | failing report | fails | failure | failure provide | failure respond | fair | fair credit | fair debt | fairly | faith | faithfully | fake | fall | fallen | falling | false | false claim | false information | false misleading | false pretense | false reporting | false representation | false statement | falsely | falsified | familiar | family | family friend | family home | family member | family xxxx | far | fargo | fargo account | fargo bank | fargo credit | fargo dealer | fargo home | fargo mortgage | fargo xxxx | farm | fashion | fast | fast forward | faster | father | father xxxx | fault | faulty | favor | fax | fax mail | fax number | fax xxxx | faxed | faxed xxxx | faxing | fay | fay servicing | fbi | fcra | fcra b | fcra fdcpa | fcra law | fcra required | fcra requires | fcra rule | fcra section | fcra state | fcra usc | fcra violation | fcra xxxx | fdcpa | fdcpa fcra | fdic | fear | feature | fed | fed loan | federal | federal agency | federal court | federal credit | federal government | federal law | federal loan | federal regulation | federal reserve | federal state | federal student | federal tax | federal trade | federally | fedloan | fedloan servicing | fee | fee account | fee added | fee applied | fee asked | fee assessed | fee associated | fee balance | fee bank | fee called | fee charge | fee charged | fee credit | fee day | fee fee | fee late | fee loan | fee month | fee nt | fee paid | fee pay | fee payment | fee penalty | fee refunded | fee removed | fee said | fee time | fee told | fee total | fee transaction | fee waived | fee xxxx | fee xxxxxxxx | fee year | feed | feedback | feel | feel comfortable | feel free | feel like | feel xxxx | feeling | fell | felony | felt | felt like | fema | female | fha | fha loan | fico | fico score | fictitious | fidelity | fiduciary | field | fifth | fifth bank | fight | fighting | figure | figured | file | file account | file bankruptcy | file claim | file complaint | file consumer | file credit | file dispute | file formal | file lawsuit | file number | file police | file report | file sent | file suit | file time | file used | file verify | file xxxx | file xxxxxxxx | filed | filed bankruptcy | filed cfpb | filed chapter | filed claim | filed complaint | filed dispute | filed lawsuit | filed police | filed report | filed xxxx | filed xxxxxxxx | filing | filing bankruptcy | filing complaint | filing dispute | filing lawsuit | filled | filling | final | final payment | finalize | finalized | finally | finally able | finally called | finally got | finally received | finally spoke | finally told | finally xxxx | finance | finance charge | finance code | finance company | finance xxxx | financed | financed xxxx | financial | financial aid | financial difficulty | financial hardship | financial information | financial institution | financial llc | financial obligation | financial protection | financial service | financial situation | financial xxxx | financially | financials | financing | financing company | financing xxxx | finding | fine | fine print | fined | finish | finished | fired | firm | firm xxxx | fit | fix | fix credit | fix error | fix issue | fix problem | fixed | fixed income | fixed rate | fixing | fl | fl xxxx | flag | flagged | flagstar | flat | flight | flood | flood insurance | floor | florida | florida xxxx | fo | focus | folk | follow | follow law | followed | followed instruction | following | following account | following day | following information | following inquiry | following item | following month | following week | following xxxx | follows | follows xxxx | followup | food | foot | footage | forbearance | force | forced | forced pay | forcing | forclosure | ford | forebearance | foreclose | foreclose home | foreclose property | foreclosed | foreclosing | foreclosure | foreclosure action | foreclosure date | foreclosure notice | foreclosure proceeding | foreclosure process | foreclosure sale | foreclosure xxxx | foreign | forever | forged | forgery | forget | forgive | forgiven | forgiveness | forgiveness program | forgot | form | form letter | form sent | form xxxx | formal | formal complaint | formally | format | forth | forward | forward xxxx | forwarded | forwarding | fought | fourth | frame | franklin | frankly | fraud | fraud account | fraud affidavit | fraud alert | fraud case | fraud charge | fraud claim | fraud department | fraud dept | fraud investigation | fraud protection | fraud report | fraud scam | fraud violation | fraud xxxx | frauding | frauding scam | fraudulent | fraudulent account | fraudulent activity | fraudulent charge | fraudulent check | fraudulent credit | fraudulent debt | fraudulent information | fraudulent inquiry | fraudulent transaction | fraudulent unauthorized | fraudulent xxxx | fraudulently | free | free annual | free credit | free report | freedom | freedom mortgage | freeze | freeze account | freeze credit | freezing | frequent | frequently | friday | friday xxxx | friday xxxxxxxx | friend | friend family | friendly | frivolous | fro | froze | frozen | frustrated | frustrating | frustration | ftc | fulfill | fulfilled | fulltime | fully | fully aware | fully paid | function | fund | fund account | fund available | fund bank | fund pay | fund received | fund released | fund returned | fund sent | fund transfer | fund xxxx | funded | funding | funding llc | funny | furious | furnish | furnished | furnisher | furnisher information | furnishers | furnishers information | furnishing | furniture | furthermore | future | future payment | g | ga | ga xxxx | gain | gain access | game | gap | gap insurance | garage | garnish | garnish wage | garnished | garnishing | garnishment | gas | gather | gave | gave credit | gave information | gave number | gave permission | gave phone | gave u | gave xxxx | gc | ge | general | general office | general xxxx | generally | generate | generated | generic | gentleman | georgia | getting | getting credit | getting loan | getting money | getting phone | getting run | getting xxxx | gift | gift card | girl | girlfriend | given | given information | given opportunity | given option | given run | given xxxx | giving | giving run | giving u | gladly | glitch | global | gm | gm financial | gmac | goal | going | going account | going credit | going file | going forth | going forward | going make | going month | going pay | going send | going xxxx | going year | gold | gon | gon na | gone | gone past | gone xxxx | good | good credit | good customer | good faith | good service | good standing | good xxxx | goodwill | got | got credit | got email | got home | got information | got letter | got loan | got money | got new | got notice | got phone | got response | got xxxx | gotten | gotten response | government | government agency | grace | grace period | graduate | graduated | graduated xxxx | graduating | graduation | gram | grand | grandmother | grant | granted | grantor | grateful | great | great deal | great lake | greater | greatly | greatly appreciate | greatly appreciated | green | green tree | greentree | grocery | gross | gross income | grossly | ground | group | group xxxx | growing | gt | guarantee | guaranteed | guess | guidance | guide | guideline | guilty | guy | gym | h | ha | hack | hacked | hacker | hadnt | half | hamp | hamp modification | hamp program | hand | handed | handle | handled | handled reporting | handling | hang | hanging | happen | happened | happened believe | happened time | happened xxxx | happening | happens | happy | harass | harassed | harassing | harassing phone | harassment | hard | hard copy | hard credit | hard inquires | hard inquiry | hard pull | hard time | harder | hardest | hardship | harm | harmed | harp | harrassing | harris | hassle | havent | havent received | hazard | hazard insurance | head | headquarters | health | health care | health insurance | health issue | hear | heard | heard company | heard xxxx | hearing | held | held accountable | held liable | held responsible | held xxxx | hello | hello xxxx | heloc | help | help getting | help matter | help need | help nt | help pay | help remove | help resolve | help situation | help thank | help told | help u | help xxxx | helped | helpful | helping | hesitate | hi | hi xxxx | hidden | hide | high | high balance | high credit | high rate | higher | higher payment | higher rate | highest | highlighted | highly | hindering | hipaa | hippa | hire | hire attorney | hired | hired attorney | hired xxxx | hiring | history | history account | history credit | history payment | history xxxx | hit | hit credit | hoa | hold | hold account | hold fund | hold hour | hold minute | hold money | hold time | hold xxxx | holder | holder xxxx | holding | holding money | hole | holiday | home | home address | home affordable | home depot | home equity | home foreclosure | home insurance | home loan | home mortgage | home owner | home phone | home preservation | home sold | home time | home told | home value | home xxxx | home xxxxxxxx | home year | homeless | homeowner | homeowner insurance | honda | honest | honestly | honor | honor request | honored | honoring | hoop | hope | hope help | hopefully | hoping | horrible | hospital | hospital xxxx | hostage | hostile | hotel | hour | hour day | hour later | hour phone | hour time | hour xxxx | house | house foreclosure | house payment | house sold | house xxxx | house xxxxxxxx | house year | household | housing | hr | hsbc | http | hud | huge | human | hung | hung called | hung phone | hunter | hunter warfield | huntington | huntington bank | hurricane | hurricane irma | hurt | hurt credit | hurting | hurting credit | husband | husband credit | husband xxxx | hyundai | ibr | ic | id | id number | id theft | id xxxx | idea | identical | identification | identification information | identification number | identified | identified xxxx | identifies | identify | identifying | identifying information | identity | identity fraud | identity stolen | identity theft | identity thief | idr | ignore | ignored | ignored request | ignoring | ii | iii | il | il xxxx | ill | illegal | illegal practice | illegally | illinois | illness | im | im sure | im trying | im xxxx | image | imagine | immediate | immediately | immediately called | immediately contacted | immediately credit | immediately paid | immediately remove | immediately told | immediately xxxx | impact | impact credit | impacted | impacted credit | impacting | impacting credit | implied | important | importantly | imposed | impossible | impound | impression | improper | improperly | improve | improvement | inability | inaccuracy | inaccurate | inaccurate account | inaccurate incomplete | inaccurate information | inaccurate reporting | inaccurately | inactive | inactivity | inadequate | inappropriate | incentive | inception | incident | include | included | included bankruptcy | included chapter | included copy | included xxxx | includes | including | including limited | including social | including xxxx | income | income based | income driven | income ratio | income tax | income xxxx | incomebased | incomebased repayment | incompetence | incompetent | incomplete | inconsistency | inconsistent | inconvenience | incorrect | incorrect account | incorrect address | incorrect information | incorrect reporting | incorrect xxxx | incorrectly | incorrectly reported | incorrectly reporting | increase | increase credit | increase payment | increase xxxx | increased | increased payment | increased xxxx | increasing | incredibly | incur | incurred | incurring | independent | indiana | indicate | indicated | indicates | indicating | indication | individual | individual account | individual xxxx | industry | ineligible | inflated | info | info credit | info xxxx | inform | information | information account | information accurate | information address | information asked | information available | information bank | information called | information collection | information company | information compromised | information concerning | information consumer | information contact | information contacted | information contained | information correct | information credit | information creditor | information date | information day | information debt | information deleted | information dispute | information disputed | information equifax | information experian | information file | information furnished | information given | information identified | information inaccurate | information including | information incorrect | information information | information led | information like | information listed | information loan | information need | information needed | information new | information nt | information obtained | information online | information party | information payment | information person | information phone | information provide | information provided | information received | information record | information regarding | information relating | information removed | information report | information reported | information reporting | information request | information requested | information required | information said | information sent | information showing | information social | information stated | information stolen | information submitted | information time | information told | information updated | information used | information verified | information verify | information want | information xxxx | information xxxxxxxx | informed | informed account | informed credit | informed loan | informed payment | informed u | informed xxxx | informing | informs | initial | initially | initiate | initiated | initiating | injured | injury | ink | innocent | input | inquire | inquired | inquirer | inquires | inquires credit | inquiring | inquiry | inquiry account | inquiry authorize | inquiry authorized | inquiry company | inquiry credit | inquiry date | inquiry deleted | inquiry equifax | inquiry fraudulent | inquiry inquiry | inquiry listed | inquiry removed | inquiry report | inquiry reporting | inquiry type | inquiry xxxx | inquiry xxxxxxxx | insane | inside | insist | insisted | insisting | insists | inspection | inspector | installation | installed | installment | installment loan | instance | instant | instantly | instead | instead discover | instead xxxx | institution | instructed | instruction | instrument | insufficient | insufficient fund | insult | insurance | insurance agent | insurance claim | insurance company | insurance coverage | insurance information | insurance paid | insurance payment | insurance policy | insurance premium | insurance property | insurance xxxx | insure | insured | insurer | integrity | intend | intended | intent | intention | intentional | intentionally | interaction | interested | interesting | internal | internally | international | internet | interstate | intervene | interview | invalid | invalidated | invested | investigate | investigate account | investigate matter | investigated | investigating | investigation | investigation account | investigation complete | investigation completed | investigation result | investigation xxxx | investigator | investment | investor | invoice | involve | involved | involved xxxx | involves | involving | inxxxxxxxx | iq | iq data | ira | irma | irrelevant | irresponsible | irs | isnt | issue | issue account | issue bank | issue called | issue company | issue credit | issue refund | issue resolved | issue time | issue told | issue xxxx | issue xxxxxxxx | issued | issued credit | issued new | issued xxxx | issuer | issuing | item | item credit | item deleted | item disputed | item information | item listed | item removed | item report | item reported | item xxxx | itemization | itemized | ive | ive tried | j | jail | jefferson | jeopardy | jersey | job | job xxxx | joined | joint | joint account | joke | jp | jp morgan | jpmorgan | jpmorgan chase | judge | judge xxxx | judgement | judgement xxxx | judgment | judgment xxxx | judicial | jump | jumped | junk | jurisdiction | justice | justification | justify | k | keeping | kept | kept asking | kept calling | kept getting | kept saying | kept telling | key | key bank | keybank | kicked | kid | kind | kindly | kitchen | knew | know | know account | know company | know credit | know debt | know fact | know going | know happened | know information | know loan | know money | know need | know nt | know payment | know person | know received | know right | know xxxx | knowing | knowingly | knowledge | knowledge account | knowledge consent | knowledge debt | knowledge xxxx | known | known xxxx | kohl | l | la | label | labeled | lack | lack communication | lady | lady xxxx | laid | lake | land | landlord | language | lapse | large | large payment | larger | largest | lastly | late | late account | late charge | late credit | late day | late fee | late late | late month | late mortgage | late notice | late paid | late pay | late paying | late payment | late reporting | late time | late xxxx | late xxxxxxxx | later | later business | later called | later date | later day | later got | later received | later told | later xxxx | later xxxxxxxx | lates | latest | laughed | law | law clear | law enforcement | law firm | law office | law requires | law state | law suit | law verify | law xxxx | lawful | lawfully | lawsuit | lawsuit filed | lawsuit xxxx | lawyer | lawyer xxxx | le | le day | le month | le xxxx | le year | lead | lead believe | leading | leaf | leak | leaked | learn | learned | learning | lease | lease agreement | lease xxxx | leased | leasing | leave | leave message | leaving | leaving message | led | led believe | led breach | ledger | left | left message | left voice | left voicemail | left xxxx | legal | legal action | legal counsel | legal department | legal document | legal fee | legal obligation | legal right | legally | legit | legitimate | lend | lender | lender xxxx | lending | lending act | lending practice | length | lengthy | lesser | let | let know | let speak | let u | letter | letter account | letter address | letter asked | letter asking | letter attached | letter attorney | letter bank | letter called | letter certified | letter chase | letter collection | letter company | letter copy | letter credit | letter creditor | letter dated | letter day | letter debt | letter dispute | letter disputing | letter email | letter equifax | letter experian | letter explaining | letter explanation | letter fargo | letter indicating | letter informing | letter letter | letter mail | letter mailed | letter phone | letter proof | letter received | letter regarding | letter request | letter requested | letter requesting | letter response | letter said | letter say | letter saying | letter send | letter sent | letter state | letter stated | letter stating | letter telling | letter today | letter told | letter transunion | letter u | letter xxxx | letter xxxxxxxx | letterhead | letting | letting know | level | levy | lexisnexis | liability | liable | liar | license | license number | license social | licensed | licensed collect | lie | lied | lien | lien credit | lien holder | lien property | lien release | lien xxxx | lieu | lieu foreclosure | life | life loan | life xxxx | lifetime | lift | lifted | light | like | like account | like credit | like file | like know | like make | like nt | like pay | like provided | like removed | like request | like said | like xxxx | likely | limit | limit xxxx | limitation | limitation expired | limited | limited section | line | line credit | line increase | line xxxx | link | linked | list | list xxxx | listed | listed account | listed credit | listed report | listed xxxx | listen | listened | listening | listing | literally | litigation | little | little notify | live | live person | live xxxx | lived | lived address | lived xxxx | lived year | living | living expense | living xxxx | llc | llc xxxx | load | loaded | loan | loan account | loan agreement | loan application | loan applied | loan approved | loan asked | loan balance | loan bank | loan called | loan car | loan care | loan charged | loan closed | loan company | loan consolidated | loan consolidation | loan contacted | loan credit | loan current | loan currently | loan date | loan day | loan debt | loan default | loan deferment | loan denied | loan department | loan depot | loan discharged | loan document | loan estimate | loan fargo | loan forbearance | loan forgiven | loan forgiveness | loan going | loan good | loan got | loan help | loan home | loan information | loan late | loan lender | loan like | loan loan | loan make | loan mod | loan modification | loan modified | loan money | loan month | loan mortgage | loan navient | loan need | loan new | loan nt | loan number | loan officer | loan originally | loan originated | loan paid | loan past | loan pay | loan paying | loan payment | loan process | loan processor | loan program | loan provider | loan rate | loan received | loan repayment | loan reported | loan said | loan school | loan sent | loan service | loan serviced | loan servicer | loan servicing | loan sold | loan started | loan taken | loan term | loan time | loan told | loan took | loan transferred | loan type | loan value | loan want | loan went | loan xxxx | loan xxxxxxxx | loan year | loancare | local | local bank | local branch | local police | local xxxx | locate | located | located xxxx | location | location xxxx | lock | locked | locked account | log | log account | logged | logged account | logging | login | long | long story | long term | long time | longer | longer able | longer business | look | look account | look credit | look forward | look like | look matter | looked | looked account | looked credit | looking | looking credit | loose | lose | lose home | losing | losing home | loss | loss mitigation | lost | lost job | lost xxxx | lot | lot money | louisiana | love | low | low income | lower | lower credit | lower monthly | lower payment | lower rate | lowered | lowered credit | lowering | lowest | loyal | loyal customer | lp | ltv | luck | lump | lump sum | lvnv | lvnv funding | lying | m | mac | machine | macy | macys | mad | madam | mae | maiden | mail check | mail copy | mail day | mail email | mail letter | mail payment | mail phone | mail receipt | mail received | mail return | mail sent | mail stating | mail xxxx | mail xxxxxxxx | mailbox | mailed | mailed certified | mailed check | mailed letter | mailed xxxx | mailed xxxxxxxx | mailing | mailing address | main | maintain | maintained | maintaining | maintenance | maintenance fee | major | major credit | majority | make | make additional | make arrangement | make change | make decision | make deposit | make difficult | make inquiry | make loan | make matter | make money | make monthly | make mortgage | make pay | make payment | make purchase | make right | make sense | make sure | make xxxx | making | making difficult | making home | making impossible | making money | making monthly | making payment | making xxxx | male | malicious | man | manage | managed | management | management company | management service | management xxxx | manager | manager said | manager told | manager xxxx | managing | mandate | mandated | mandatory | manger | manipulated | manner | manner soon | manual | manually | mark | mark credit | mark verify | marked | market | market value | marketing | marriage | married | maryland | massachusetts | massive | master | master card | mastercard | match | matched | matching | material | math | matter | matter fact | matter resolved | matter thank | matter worse | matter xxxx | maturity | maturity date | max | maximum | maybe | mcm | md | md xxxx | mean | mean time | meaning | meant | meantime | measure | mediation | medical | medical collection | medical debt | medical information | medical insurance | medical issue | medical provider | medical record | medical xxxx | medication | medium | meet | meet requirement | meeting | member | member xxxx | membership | membership fee | membership reward | mental | mention | mentioned | merchandise | merchant | merchant xxxx | merely | merged | merit | mess | message | message left | message saying | message stating | message xxxx | messed | met | met requirement | method | method verification | mi | mi xxxx | michigan | mid | mid xxxx | middle | midland | midland credit | midland funding | midwest | midwest recovery | mile | mileage | military | million | million potential | min | mind | minimal | minimum | minimum balance | minimum payment | minnesota | minor | minus | minute | minute later | minute phone | misapplied | miscellaneous | misconduct | mishandled | mishandled information | mishandling | misinformation | misinformed | mislead | misleading | misleading information | misled | misplaced | misrepresentation | misrepresented | miss | miss payment | missed | missed payment | missed xxxx | missing | missing payment | missouri | mistake | mistake appear | mistake xxxx | mistaken | mistakenly | misunderstanding | mitigation | mixed | mn | mn xxxx | mo | mo xxxx | mobile | mobile app | mod | model | modification | modification agreement | modification application | modification approved | modification denied | modification document | modification loan | modification mortgage | modification package | modification payment | modification process | modification program | modification request | modification review | modification time | modification told | modification xxxx | modification xxxxxxxx | modified | modify | modify loan | mohela | mold | mom | moment | monday | monday xxxx | monday xxxxxxxx | monetary | monetary damage | money | money account | money available | money bank | money called | money checking | money credit | money day | money deposited | money escrow | money going | money loan | money make | money money | money month | money need | money nt | money order | money owe | money owed | money paid | money pay | money received | money refunded | money returned | money said | money sent | money taken | money time | money told | money transfer | money want | money went | money xxxx | moneygram | monies | monitor | monitor credit | monitored | monitoring | monitoring service | month | month account | month ago | month asked | month balance | month bank | month breach | month called | month charged | month contacted | month credit | month date | month day | month late | month later | month loan | month make | month month | month mortgage | month nt | month paid | month passed | month past | month pay | month paying | month payment | month period | month prior | month receive | month received | month said | month sent | month statement | month time | month told | month trying | month went | month xxxx | month xxxxxxxx | month year | monthly | monthly basis | monthly fee | monthly income | monthly mortgage | monthly payment | monthly service | monthly statement | monthly xxxx | morgan | morgan chase | morning | morning xxxx | morning xxxxxxxx | mortage | mortgage | mortgage account | mortgage assistance | mortgage balance | mortgage bank | mortgage broker | mortgage company | mortgage credit | mortgage current | mortgage fargo | mortgage fraud | mortgage holder | mortgage home | mortgage insurance | mortgage lender | mortgage llc | mortgage loan | mortgage modification | mortgage month | mortgage mortgage | mortgage note | mortgage paid | mortgage payment | mortgage property | mortgage rate | mortgage service | mortgage servicer | mortgage servicing | mortgage sold | mortgage statement | mortgage time | mortgage told | mortgage transferred | mortgage xxxx | mortgage xxxxxxxx | mortgage year | mortgagee | mortgagor | mother | mother xxxx | motion | motor | moved | moved xxxx | moving | moving forward | mr | mr cooper | mtg | multiple | multiple account | multiple attempt | multiple dispute | multiple inquiry | multiple letter | multiple occasion | multiple phone | multiple request | multiple time | mutual | n | n xxxx | na | named | named xxxx | nasty | nation | nation star | national | national bank | national credit | nationstar | nationstar mortgage | nationstar xxxx | nationwide | natural | nature | navient | navient loan | navient xxxx | navy | navy federal | nc | nc xxxx | ncc | nd | ne | near | nearly | nearly month | nearly xxxx | nearly year | necessary | need | need additional | need assistance | need contact | need corrected | need credit | need deleted | need help | need information | need investigated | need know | need make | need money | need pay | need provide | need remove | need removed | need send | need speak | need stop | need xxxx | needed | needed contact | needed make | needed pay | needed send | needed xxxx | needing | needle | needle say | negative | negative account | negative balance | negative credit | negative impact | negative information | negative item | negative mark | negative remark | negative report | negative reporting | negative xxxx | negatively | negatively affected | negatively affecting | negatively credit | negatively impacted | neglected | negligence | negligent | negotiate | negotiated | negotiating | negotiation | negotiator | neighbor | neighborhood | nelnet | net | netspend | network | nevada | new | new account | new address | new balance | new bank | new car | new card | new checking | new collection | new company | new credit | new home | new information | new jersey | new job | new law | new lender | new loan | new mortgage | new owner | new payment | new servicer | new vehicle | new xxxx | new york | newly | news | nfcu | nice | night | nightmare | nissan | nj | nj xxxx | non | non payment | noncompliance | nonetheless | nonexistent | nonpayment | nonsense | normal | normally | north | north carolina | notarized | notary | notation | note | note account | note mortgage | note xxxx | noted | notice | notice account | notice collection | notice consumer | notice credit | notice debt | notice default | notice dispute | notice mail | notice received | notice sent | notice xxxx | noticed | noticed account | noticed collection | noticed credit | noticed xxxx | notification | notification xxxx | notified | notified credit | notified xxxx | notify | notify public | notifying | noting | npv | nr | nsf | nsf fee | nt | nt able | nt accept | nt access | nt account | nt afford | nt allow | nt answer | nt apply | nt believe | nt belong | nt care | nt credit | nt exist | nt feel | nt going | nt happen | nt heard | nt help | nt information | nt know | nt let | nt like | nt loan | nt make | nt match | nt matter | nt money | nt need | nt nt | nt owe | nt paid | nt pay | nt payment | nt provide | nt qualify | nt really | nt receive | nt received | nt recognize | nt remember | nt remove | nt report | nt respond | nt right | nt say | nt send | nt stop | nt tell | nt think | nt time | nt understand | nt use | nt verify | nt want | nt work | nt xxxx | null | number | number account | number address | number asked | number bank | number called | number calling | number card | number company | number contact | number credit | number date | number email | number file | number gave | number given | number listed | number nt | number number | number payment | number person | number phone | number provided | number said | number sent | number time | number told | number used | number xxxx | number xxxxxxxx | number year | numerous | numerous attempt | numerous complaint | numerous letter | numerous occasion | numerous phone | numerous time | nv | nv xxxx | ny | ny xxxx | oath | obligated | obligated pay | obligation | obligation pay | obtain | obtain credit | obtain information | obtain loan | obtained | obtained xxxx | obtaining | obvious | obviously | occ | occasion | occasion xxxx | occur | occurred | occurred xxxx | occurred xxxxxxxx | occurrence | occurring | occurs | ocwen | ocwen loan | ocwen xxxx | odd | offense | offer | offer credit | offer settlement | offer xxxx | offered | offered pay | offered xxxx | offering | office | office president | office provide | office told | office xxxx | officer | officer xxxx | official | officially | offset | oh | oh xxxx | ohio | ok | okay | old | old account | old address | old credit | old debt | old xxxx | older | ombudsman | onetime | ongoing | online | online access | online account | online banking | online chat | online dispute | online pay | online payment | online phone | online statement | online xxxx | ontime | ontime payment | onxxxxxxxx | open | open account | open bank | open checking | open collection | open credit | open date | open dispute | open investigation | open new | open xxxx | open xxxxxxxx | opened | opened account | opened checking | opened credit | opened date | opened new | opened xxxx | opened xxxxxxxx | opening | opening account | opening new | operate | operating | operation | operator | opinion | opportunity | opposed | opposite | opt | opted | option | option available | option pay | option xxxx | ordeal | order | order avoid | order make | order pay | order xxxx | ordered | oregon | organization | origin | original | original account | original application | original balance | original company | original complaint | original consumer | original contract | original creditor | original date | original debt | original debtor | original dispute | original document | original documentation | original lender | original loan | original mortgage | original note | original payment | original signed | original xxxx | originally | originally xxxx | originated | originated xxxx | origination | originator | outcome | outdated | outlined | outrageous | outright | outside | outsourcing | outstanding | outstanding balance | outstanding debt | overage | overall | overcharge | overcharged | overdraft | overdraft charge | overdraft fee | overdraft protection | overdrawn | overdue | overnight | overpaid | overpayment | overseas | oversight | owe | owe account | owe balance | owe company | owe debt | owe money | owe xxxx | owed | owed account | owed debt | owed money | owed xxxx | owes | owing | owned | owned xxxx | owner | owner insurance | owner xxxx | ownership | owning | owns | p | p xxxx | pa | pa xxxx | pacific | package | packet | page | page xxxx | paid | paid account | paid agreed | paid balance | paid bank | paid called | paid car | paid card | paid charge | paid closed | paid collection | paid company | paid credit | paid date | paid day | paid debt | paid escrow | paid fee | paid insurance | paid late | paid letter | paid loan | paid money | paid month | paid monthly | paid mortgage | paid nt | paid original | paid paid | paid payment | paid received | paid tax | paid time | paid told | paid total | paid xxxx | paid xxxxxxxx | paid year | pain | pal | paper | paper statement | paper work | paperless | paperwork | paperwork sent | paperwork xxxx | paragraph | parcel | parent | parking | partial | partial payment | partially | participate | particular | particularly | partner | party | party collection | party company | party debt | party involved | party xxxx | pas | passed | passed away | passing | passport | password | past | past account | past balance | past credit | past date | past day | past month | past payment | past statute | past week | past xxxx | past xxxxxxxx | past year | patience | patient | pattern | pay | pay account | pay additional | pay balance | pay bank | pay car | pay card | pay charge | pay check | pay credit | pay day | pay debt | pay entire | pay extra | pay fee | pay insurance | pay late | pay loan | pay minimum | pay money | pay month | pay monthly | pay mortgage | pay nt | pay online | pay owe | pay paid | pay pal | pay pay | pay payment | pay phone | pay property | pay remaining | pay rent | pay said | pay service | pay stub | pay student | pay tax | pay time | pay told | pay xxxx | pay xxxxxxxx | pay year | payable | paycheck | payday | payday loan | payed | payee | paying | paying account | paying balance | paying credit | paying debt | paying loan | paying month | paying monthly | paying mortgage | paying student | paying time | paying xxxx | paying year | payment | payment able | payment accepted | payment account | payment advised | payment afford | payment agreed | payment agreement | payment applied | payment arrangement | payment asked | payment automatically | payment balance | payment bank | payment based | payment beginning | payment called | payment car | payment card | payment charge | payment charged | payment check | payment checking | payment cleared | payment come | payment company | payment contact | payment contacted | payment continue | payment credit | payment credited | payment current | payment date | payment day | payment debt | payment deducted | payment end | payment escrow | payment explained | payment fact | payment fargo | payment fee | payment going | payment got | payment history | payment home | payment immediately | payment increase | payment increased | payment information | payment informed | payment instead | payment insurance | payment issue | payment late | payment letter | payment like | payment loan | payment make | payment making | payment method | payment missed | payment money | payment month | payment monthly | payment mortgage | payment navient | payment need | payment needed | payment new | payment nt | payment online | payment option | payment order | payment original | payment paid | payment past | payment pay | payment paying | payment payment | payment phone | payment plan | payment plus | payment posted | payment principal | payment prior | payment process | payment processed | payment receive | payment received | payment record | payment refused | payment removed | payment report | payment reported | payment reporting | payment request | payment requested | payment required | payment returned | payment said | payment schedule | payment scheduled | payment sent | payment set | payment showing | payment started | payment stated | payment statement | payment status | payment student | payment submitted | payment taken | payment term | payment time | payment timely | payment today | payment told | payment total | payment tried | payment u | payment want | payment way | payment week | payment went | payment xxxx | payment xxxxxxxx | payment xxxxxxxxxxxx | payment year | payoff | payoff loan | payoff quote | payoff statement | payoff xxxx | paypal | paypal account | paypal credit | payroll | paystubs | pc | penalized | penalty | penalty perjury | pending | pending transaction | penfed | pennsylvania | penny | penny mac | pennymac | pension | people | people like | people xxxx | percent | percentage | perfect | perfect credit | perform | performance | performed | period | period month | period payment | period time | period xxxx | period xxxxxxxx | perjury | permanent | permanent modification | permanently | permissible | permissible purpose | permission | permit | permitted | person | person called | person phone | person said | person spoke | person told | person xxxx | personal | personal account | personal banker | personal business | personal check | personal checking | personal credit | personal data | personal financial | personal info | personal information | personal loan | personal phone | personal property | personally | personnel | pertaining | pertinent | petition | phh | phh mortgage | phone | phone account | phone asked | phone called | phone company | phone conversation | phone customer | phone day | phone email | phone hour | phone instead | phone letter | phone line | phone mail | phone message | phone nt | phone number | phone payment | phone received | phone record | phone representative | phone said | phone sent | phone service | phone time | phone told | phone work | phone xxxx | phone xxxxxxxx | phoned | photo | physical | physical address | physical verification | physically | physician | pi | pick | picked | picture | piece | pin | pin number | pinnacle | place | place credit | place employment | place xxxx | placed | placed account | placed collection | placed credit | placed fraud | placed hold | placed insurance | placed xxxx | placement | placing | plain | plaintiff | plan | plan payment | plan xxxx | planned | planning | plate | platform | platinum | platinum card | play | played | playing | plea | pleading | plenty | plus | plus loan | plus xxxx | pm | pmi | pmt | pnc | pnc bank | pnc mortgage | po | po box | poa | point | point contact | point credit | point time | point told | point xxxx | pointed | police | police department | police report | policy | policy xxxx | pool | poor | poor customer | poorly | pop | portal | portfolio | portfolio recovery | portfolio service | portfolio servicing | portion | portion thereof | position | positive | positive balance | positively | posse | possession | possibility | possible | possible thank | possibly | post | post account | post credit | post office | postal | postal service | posted | posted account | posted credit | posted xxxx | posting | postpone | postponed | potential | potential risk | potentially | power | power attorney | pra | practice | practice act | practice xxxx | pre | preapproval | preapproved | predatory | predatory lending | predatory loan | prefer | preferred | prejudice | premier | premise | premium | prepaid | prepaid card | prepare | prepared | preparing | prepayment | presence | present | presented | presently | preservation | president | president xxxx | press | pressed | pressler | pressure | pressured | prestige | pretense | pretty | prevent | prevented | preventing | prevention | previous | previous address | previous complaint | previous month | previous payment | previously | previously deleted | previously disputed | price | primary | primary residence | prime | principal | principal balance | principal loan | principal payment | principal reduction | principal xxxx | principle | principle balance | printed | printout | prior | prior closing | prior date | prior xxxx | prior xxxxxxxx | priority | privacy | privacy right | private | private information | private loan | private student | privilege | pro | probably | probate | problem | problem account | problem credit | problem resolved | problem xxxx | procedure | procedure used | proceed | proceeded | proceeded tell | proceeding | proceeds | process | process loan | process payment | process request | process time | process told | process xxxx | processed | processed xxxx | processing | processing fee | processor | procollect | produce | produced | product | prof | professional | profile | profit | profit loss | program | program xxxx | progress | prohibited | prohibits | project | promise | promised | promised xxxx | promising | promissory | promissory note | promo | promotion | promotional | promotional balance | promotional offer | prompt | prompted | promptly | promptly delete | proof | proof account | proof claim | proof contract | proof debt | proof identity | proof insurance | proof original | proof payment | proof sent | proof verification | proof xxxx | proper | proper documentation | proper investigation | proper validation | properly | property | property address | property located | property sold | property tax | property value | property xxxx | property xxxxxxxx | proposal | proposed | protect | protect consumer | protected | protecting | protection | protection act | protection bureau | protection law | protection service | protocol | prove | prove debt | proved | proven | provide | provide additional | provide address | provide consumer | provide copy | provide credit | provide document | provide documentation | provide evidence | provide following | provide information | provide notice | provide original | provide proof | provide proper | provide requested | provide service | provide u | provide validation | provide verification | provide written | provide xxxx | provided | provided copy | provided credit | provided document | provided documentation | provided information | provided letter | provided proof | provided requested | provided xxxx | provider | provider xxxx | provides | providing | providing information | providing proof | proving | provision | provisional | provisional credit | pslf | pst | public | public nearly | public record | public service | publish | published | pull | pull credit | pulled | pulled credit | pulled xxxx | pulling | pulling credit | punished | punitive | purchase | purchase home | purchase new | purchase price | purchase vehicle | purchase xxxx | purchase xxxxxxxx | purchased | purchased car | purchased debt | purchased home | purchased loan | purchased vehicle | purchased xxxx | purchaser | purchasing | purchasing home | purported | purpose | purposely | pursuant | pursuant fair | pursue | pursue legal | pursued | pursuing | push | pushed | pushing | putting | qualification | qualified | qualified written | qualify | qualify loan | qualifying | qualifying payment | quality | quarter | question | question asked | question regarding | question xxxx | questionable | questioned | questioning | quick | quicken | quicken loan | quickly | quit | quit claim | quite | quote | quoted | qwr | r | race | raise | raised | raising | ran | ran credit | random | range | rarely | rate | rate loan | rate lock | rate mortgage | rate payment | rate reduction | rate xxxx | rate year | rating | ratio | reach | reach xxxx | reached | reached xxxx | reaching | read | reading | ready | reaffirmation | reaffirmed | reaged | reaging | real | real estate | real property | reality | realize | realized | realizing | really | really need | really nt | realtor | reappeared | reapplied | reapply | reason | reason account | reason believe | reason denial | reason given | reason xxxx | reasonable | reasonable investigation | reasonable procedure | reasonably | reasoning | reassured | rebate | rebuild | rec | recall | recast | receipt | receipt letter | receipt payment | receipt xxxx | receivable | receivables | receive | receive bonus | receive check | receive credit | receive email | receive fund | receive information | receive letter | receive mail | receive money | receive notice | receive notification | receive payment | receive phone | receive refund | receive response | receive statement | receive xxxx | received | received account | received alert | received bank | received called | received card | received certified | received check | received collection | received communication | received company | received confirmation | received copy | received correspondence | received credit | received day | received debt | received dispute | received document | received documentation | received email | received final | received form | received fund | received information | received late | received letter | received loan | received mail | received message | received money | received multiple | received new | received notice | received notification | received numerous | received offer | received paper | received paperwork | received payment | received phone | received refund | received reply | received request | received response | received statement | received time | received today | received told | received voicemail | received written | received xxxx | received xxxxxxxx | receiver | receives | receiving | receiving letter | receiving notice | receiving payment | receiving phone | receiving statement | receiving xxxx | recent | recent xxxx | recently | recently applied | recently pulled | recently received | recently sent | recently xxxx | recertification | recession | recieve | recieved | recipient | recognize | recognized | recollection | recommend | recommendation | recommended | record | record account | record company | record credit | record file | record information | record payment | record phone | record xxxx | record xxxxxxxx | recorded | recorded conversation | recorded line | recorded message | recorded phone | recorded xxxx | recorder | recording | recourse | recover | recovered | recovering | recovery | recovery associate | recovery company | recovery service | recovery solution | recovery xxxx | rectified | rectify | rectify situation | recurring | recurring payment | red | red flag | redeem | redemption | reduce | reduced | reduced payment | reducing | reduction | ref | ref xxxx | refer | reference | reference account | reference number | reference xxxx | referenced | referencing | referral | referred | referred xxxx | referring | refi | refinance | refinance home | refinance loan | refinance mortgage | refinance xxxx | refinanced | refinanced home | refinanced mortgage | refinancing | reflect | reflected | reflected credit | reflecting | reflects | refund | refund check | refund fee | refund money | refund xxxx | refunded | refunding | refusal | refusal pay | refuse | refuse acknowledge | refuse correct | refuse help | refuse pay | refuse provide | refuse remove | refuse send | refused | refused accept | refused help | refused pay | refused payment | refused provide | refused refund | refused remove | refused send | refusing | refusing remove | reg | regard | regard account | regard debt | regard matter | regard xxxx | regarding | regarding account | regarding alleged | regarding credit | regarding debt | regarding dispute | regarding issue | regarding loan | regarding matter | regarding payment | regarding xxxx | regardless | region | region bank | regional | register | registered | registration | regular | regular mail | regular monthly | regular payment | regularly | regulation | regulator | regulatory | rehabilitation | reimburse | reimbursed | reimbursement | reinserted | reinsertion | reinstate | reinstated | reinstatement | reinvestigate | reinvestigation | reissue | reissued | reiterated | reject | rejected | rejection | related | related account | relates | relating | relation | relationship | relationship manager | relative | release | release fund | release lien | released | releasing | relevant | reliable | relied | relief | relocation | rely | remain | remain credit | remainder | remained | remaining | remaining balance | remains | remains credit | remark | remark credit | remedy | remedy available | remember | remind | reminded | reminder | removal | remove | remove account | remove charge | remove collection | remove credit | remove debt | remove fraudulent | remove hard | remove inaccurate | remove incorrect | remove information | remove inquiry | remove item | remove late | remove negative | remove pmi | remove report | remove unauthorized | remove xxxx | removed | removed account | removed credit | removed immediately | removed report | removed xxxx | removed xxxxxxxx | removing | rendered | renew | renewal | renewed | rent | rental | rental property | rented | renter | renting | reopen | reopen account | reopened | rep | rep said | rep spoke | rep stated | rep told | rep xxxx | repaid | repair | repaired | repay | repay loan | repaye | repaying | repayment | repayment option | repayment plan | repayment program | repeat | repeated | repeated attempt | repeated request | repeatedly | repeatedly asked | repeatedly called | repeatedly told | repeating | replace | replaced | replacement | replacement card | replied | reply | repo | report | report account | report accurate | report address | report agency | report asked | report attached | report authorize | report balance | report belong | report bureau | report called | report collection | report company | report complete | report consumer | report contacted | report correct | report credit | report date | report dated | report day | report debt | report discovered | report dispute | report disputed | report equifax | report error | report experian | report false | report file | report filed | report following | report fraud | report fraudulent | report ftc | report help | report identity | report immediately | report inaccurate | report incorrect | report information | report inquiry | report item | report knowledge | report late | report letter | report like | report loan | report month | report need | report negative | report notice | report noticed | report nt | report number | report online | report open | report paid | report past | report paying | report payment | report provide | report provided | report pulled | report received | report reflect | report remove | report removed | report report | report reported | report reporting | report request | report requested | report result | report said | report saw | report say | report score | report send | report sent | report showed | report showing | report state | report stated | report stating | report submitted | report thank | report time | report today | report told | report transunion | report tried | report understanding | report updated | report violation | report want | report xxxx | report xxxxxxxx | report year | reported | reported account | reported accurately | reported collection | reported correctly | reported credit | reported day | reported debt | reported delinquent | reported fraud | reported information | reported late | reported payment | reported xxxx | reported xxxxxxxx | reporting | reporting account | reporting accurate | reporting act | reporting agency | reporting balance | reporting breach | reporting bureau | reporting collection | reporting company | reporting correctly | reporting credit | reporting date | reporting day | reporting debt | reporting false | reporting fraudulent | reporting inaccurate | reporting incorrect | reporting incorrectly | reporting information | reporting late | reporting loan | reporting negative | reporting negatively | reporting payment | reporting service | reporting wrong | reporting xxxx | repository | repossessed | repossession | represent | representation | representative | representative advised | representative asked | representative called | representative informed | representative named | representative phone | representative said | representative spoke | representative stated | representative supervisor | representative told | representative xxxx | represented | representing | representing xxxx | represents | reputable | reputation | request | request account | request assistance | request consumer | request copy | request credit | request day | request denied | request documentation | request information | request investigate | request investigation | request loan | request mortgage | request payment | request proof | request provide | request received | request refund | request remove | request sent | request submitted | request time | request validation | request verification | request writing | request xxxx | request xxxxxxxx | requested | requested account | requested consumer | requested copy | requested credit | requested document | requested documentation | requested information | requested letter | requested loan | requested payment | requested proof | requested remove | requested send | requested speak | requested time | requested validation | requested verification | requested xxxx | requested xxxxxxxx | requesting | requesting information | requesting payment | requesting proof | requesting validation | requesting verification | requesting xxxx | require | required | required document | required fcra | required federal | required information | required law | required pay | required payment | required promptly | required provide | required section | required xxxx | requirement | requirement xxxx | requires | requiring | rescind | rescinded | rescission | research | research department | researched | researching | reseller | resend | resent | reservation | reserve | reset | reside | resided | residence | resident | residential | resolution | resolution matter | resolution xxxx | resolve | resolve debt | resolve issue | resolve manner | resolve matter | resolve problem | resolve situation | resolved | resolved issue | resolved xxxx | resolving | resolving issue | resolving matter | resort | resource | respa | respect | respectfully | respectfully request | respectfully xxxx | respectively | respond | respond day | respond letter | respond request | responded | responded letter | responded request | responded xxxx | respondent | responding | responds | response | response cfpb | response company | response complaint | response day | response dispute | response letter | response received | response request | response sent | response xxxx | response xxxxxxxx | responsibility | responsibility individual | responsible | responsible charge | responsible debt | rest | restaurant | restitution | restore | restored | restricted | restriction | resubmit | resubmitted | result | result identity | result investigation | result reinvestigation | result xxxx | resulted | resulted identity | resulting | resume | resurgent | retail | retailer | retain | retained | retaliation | retention | retired | retirement | retrieve | return | return fund | return money | return phone | return receipt | return xxxx | returned | returned account | returned check | returned item | returned payment | returned phone | returned xxxx | returning | revealed | revenue | reversal | reverse | reverse charge | reverse mortgage | reversed | review | review account | review credit | review xxxx | reviewed | reviewed account | reviewed credit | reviewing | reviewing credit | revised | revoked | revolving | reward | reward card | reward point | rid | ridiculous | right | right away | right collect | right consumer | right dispute | right fair | right fcra | right request | right thing | right violated | right xxxx | ring | rip | ripped | rise | risk | rma | rms | road | robbed | robo | role | roll | rolled | roof | room | roommate | roughly | round | roundpoint | route | routinely | routing | routing number | row | rude | rudely | ruin | ruin credit | ruined | ruined credit | ruining | ruining credit | rule | rule regulation | ruled | ruling | run | run credit | runaround | running | rush | rushcard | rushmore | s | s xxxx | sad | safe | safety | said | said account | said asked | said bank | said ca | said called | said check | said company | said contact | said credit | said day | said debt | said didnt | said dont | said going | said help | said information | said loan | said look | said make | said money | said need | said needed | said nt | said ok | said owe | said owed | said paid | said pay | said payment | said problem | said receive | said received | said record | said remove | said said | said send | said sent | said thing | said time | said told | said want | said wanted | said way | said xxxx | said yes | salary | sale | sale date | sale home | sale property | sale xxxx | salesman | sallie | sallie mae | santander | santander bank | santander consumer | sat | satisfaction | satisfactory | satisfied | satisfy | saturday | saturday xxxx | saturday xxxxxxxx | save | save home | saved | saving | saving account | saw | saw xxxx | say | say account | say need | say nt | say owe | say xxxx | saying | saying account | saying nt | saying owe | saying owed | saying payment | saying xxxx | sc | sc xxxx | scam | scam using | scammed | scammer | scamming | scan | scanned | scare | scared | scenario | schedule | scheduled | scheduled payment | scheduled xxxx | scheme | school | school loan | school xxxx | score | score credit | score drop | score dropped | score low | score point | score went | score xxxx | scoring | scottrade | scra | screen | screen shot | screenshot | screenshots | screwed | script | sd | sd xxxx | se | search | searched | searching | sears | sec | sec b | sec xxxx | second | second complaint | second letter | second mortgage | second payment | second time | second xxxx | secondary | secondly | secret | secretary | secretary state | section | section b | section c | section consumer | section fair | section fcra | section required | section section | section shall | section xxxx | secure | secure message | secured | secured credit | securing | security | security breach | security card | security deposit | security freeze | security number | security question | security xxxx | seeing | seek | seek legal | seeking | seemingly | seen | select | select portfolio | selected | selene | selene finance | self | sell | sell home | sell house | sell property | seller | selling | semester | senator | send | send check | send collection | send copy | send credit | send document | send documentation | send email | send information | send letter | send mail | send money | send new | send notice | send payment | send proof | send statement | send u | send updated | send written | send xxxx | sender | sending | sending email | sending letter | sending payment | sends | senior | senior citizen | sense | sensitive | sensitive information | sent | sent account | sent address | sent bank | sent certified | sent check | sent collection | sent company | sent copy | sent credit | sent day | sent debt | sent dispute | sent document | sent documentation | sent email | sent equifax | sent fax | sent form | sent information | sent letter | sent loan | sent mail | sent message | sent money | sent multiple | sent new | sent notice | sent numerous | sent paperwork | sent payment | sent police | sent proof | sent received | sent request | sent response | sent second | sent statement | sent time | sent u | sent validation | sent written | sent wrong | sent xxxx | sent xxxxxxxx | sentence | separate | separate occasion | separated | separately | seq | series | seriously | serve | served | server | service | service account | service agent | service called | service charge | service company | service credit | service department | service fax | service fee | service line | service llc | service loan | service member | service nt | service number | service paid | service person | service phone | service provided | service provider | service received | service rendered | service rep | service representative | service said | service time | service told | service xxxx | service xxxxxxxx | serviced | serviced xxxx | servicer | servicer loan | servicer xxxx | servicers | servicing | servicing company | servicing llc | servicing loan | servicing mortgage | servicing xxxx | serving | session | set | set account | set auto | set automatic | set forth | set payment | set xxxx | seterus | setting | settle | settle account | settle debt | settled | settled xxxx | settlement | settlement agreement | settlement offer | settlement xxxx | settling | setup | seven | seven year | severe | severely | shady | shall | shall promptly | shame | shape | share | shared | sharing | sheet | shellpoint | shellpoint mortgage | sheriff | sheriff sale | ship | shipped | shipping | shock | shocked | shocked reviewed | shop | shopping | short | short sale | shortage | shortly | shot | shouldnt | showed | showed credit | showed xxxx | showing | showing account | showing balance | showing credit | showing late | showing paid | showing payment | showing report | showing xxxx | shown | shown credit | shut | sick | sign | sign contract | sign document | signature | signature card | signature showing | signature xxxx | signed | signed agreement | signed consumer | signed contract | signed document | signed loan | signed xxxx | signed xxxxxxxx | signer | significant | significantly | signing | signup | similar | simple | simply | simultaneously | sincerely | sincerely xxxx | single | single month | single mother | single point | sir | sir madam | sister | sit | site | site xxxx | sitting | situation | situation told | situation xxxx | size | skip | sl | sleep | slightly | slip | slow | small | small business | small claim | smaller | smart | soc | soc sec | socalled | social | social medium | social security | sofi | soft | software | sold | sold account | sold collection | sold company | sold debt | sold home | sold house | sold loan | sold mortgage | sold xxxx | sole | solely | solicitation | solid | solution | solve | solve problem | solved | somebody | somewhat | son | son xxxx | soon | soon possible | sooner | sorry | sort | sought | sound | sound like | sounded | source | south | southwest | southwest credit | space | speak | speak manager | speak representative | speak supervisor | speak xxxx | speaking | speaking representative | speaking xxxx | special | specialist | specialist xxxx | specialized | specialized loan | specific | specific information | specifically | specifically asked | specified | specify | speedy | spelled | spelling | spend | spending | spent | spent hour | spent xxxx | spite | split | spoke | spoke agent | spoke customer | spoke different | spoke manager | spoke phone | spoke rep | spoke representative | spoke said | spoke supervisor | spoke told | spoke xxxx | spoken | spoken xxxx | spot | spouse | spread | spring | sps | square | ssi | ssn | ssn xxxx | st | staff | stage | stall | stalling | stamp | stamped | stand | standard | standing | star | start | start making | start paying | start process | start xxxx | started | started calling | started getting | started making | started paying | started process | started receiving | started xxxx | started xxxxxxxx | starting | starting xxxx | starting xxxxxxxx | state | state account | state attorney | state california | state debt | state farm | state federal | state florida | state information | state law | state owe | state payment | state tax | state texas | state xxxx | statebridge | stated | stated account | stated credit | stated debt | stated information | stated letter | stated loan | stated need | stated needed | stated nt | stated owed | stated payment | stated received | stated sent | stated xxxx | statement | statement account | statement balance | statement called | statement consumer | statement credit | statement date | statement dated | statement mail | statement month | statement payment | statement received | statement sent | statement showed | statement showing | statement thought | statement xxxx | statement xxxxxxxx | stating | stating account | stating debt | stating information | stating loan | stating needed | stating nt | stating owe | stating owed | stating payment | stating received | stating verified | stating xxxx | station | statue | statue limitation | status | status account | status closed | status credit | status date | status loan | status open | status paid | status update | status xxxx | statute | statute limitation | statutory | stay | stay home | stayed | staying | steal | stealing | stellar | step | stipulation | stock | stole | stolen | stop | stop calling | stop collection | stop contacting | stop foreclosure | stop harassing | stop payment | stop reporting | stopped | stopped paying | stopping | storage | store | store card | store credit | store xxxx | stored | story | story short | straight | straight answer | straighten | straightened | strange | street | stress | stressed | stressful | strict | strong | strongly | structure | struggle | struggled | struggling | stub | stuck | student | student loan | study | stuff | stupid | subject | subjected | submission | submit | submit complaint | submit dispute | submit request | submitted | submitted application | submitted complaint | submitted dispute | submitted document | submitted documentation | submitted information | submitted letter | submitted paperwork | submitted payment | submitted request | submitted xxxx | submitted xxxxxxxx | submitting | submitting complaint | subparagraph | subpoena | subscription | subsection | subsection section | subsequent | subsequently | subsidiary | subsidized | substantial | substantially | substantiate | success | successful | successfully | successor | sudden | suddenly | sue | sued | suffer | suffered | suffering | sufficient | sufficient fund | suggest | suggested | suggestion | suing | suit | sum | summary | summer | summons | sun | sunday | suntrust | suntrust bank | superior | superior court | supervisor | supervisor named | supervisor said | supervisor spoke | supervisor supervisor | supervisor told | supervisor xxxx | supplied | supply | support | support claim | support xxxx | supported | supporting | supporting document | supporting documentation | suppose | supposed | supposed debt | supposedly | supreme | supreme court | sure | sure happened | sure xxxx | surely | surplus | surprise | surprised | surrender | survey | survive | suspect | suspected | suspended | suspense | suspense account | suspicious | sustained | switch | switched | switching | sworn | synchrony | synchrony bank | table | tacked | tactic | tag | taken | taken account | taken advantage | taken care | taken credit | taken loan | taken place | taken xxxx | taking | taking advantage | taking care | taking money | taking payment | taking time | talk | talk supervisor | talk xxxx | talked | talked xxxx | talking | talking xxxx | tape | target | targeted | task | tax | tax escrow | tax insurance | tax lien | tax paid | tax payment | tax refund | tax return | tax xxxx | tax xxxxxxxx | tax year | tcf | td | td bank | team | tear | tech | technical | technically | technology | telephone | telephone number | telephone xxxx | tell | tell account | tell nt | tell u | tell xxxx | teller | telling | telling u | temporarily | temporary | temporary credit | tenant | tennessee | term | term agreement | term condition | term loan | term month | term xxxx | terminate | terminated | termination | termination fee | terrible | test | texas | texas xxxx | text | text message | th | th e | thank | thank advance | thank assistance | thank help | thank time | thank xxxx | thanks | thanks xxxx | thats | theft | theft account | theft affidavit | theft fraud | theft identity | theft report | theft victim | theft xxxx | thereof | theyre | theyve | thief | thier | thing | thing credit | thing happened | thing like | thing xxxx | think | think fair | think xxxx | thinking | thirdparty | thirty | thirty day | thorough | thoroughly | thought | thousand | thousand dollar | threat | threaten | threatened | threatening | threatening letter | threshold | threw | throw | thrown | thursday | thursday xxxx | thursday xxxxxxxx | ticket | ticket xxxx | tied | tier | til | tila | till | till xxxx | time | time able | time account | time ago | time ask | time asked | time asking | time bank | time called | time collection | time company | time contact | time contacted | time credit | time customer | time date | time day | time debt | time different | time dispute | time finally | time frame | time getting | time given | time got | time help | time home | time informed | time issue | time job | time late | time left | time limit | time loan | time make | time money | time month | time mortgage | time need | time nt | time paid | time past | time pay | time payment | time period | time phone | time purchase | time receive | time received | time regarding | time representative | time request | time requested | time response | time said | time say | time send | time sent | time speak | time spoke | time stated | time tell | time time | time today | time told | time took | time tried | time try | time trying | time want | time week | time went | time work | time xxxx | time xxxxxxxx | time year | timeframe | timeline | timely | timely fashion | timely manner | timely payment | timeshare | timing | tire | tired | title | title company | title loan | title xxxx | titled | tn | tn xxxx | today | today called | today date | today received | today told | today xxxx | today xxxxxxxx | today xxxxxxxxxxxx | told | told able | told account | told agent | told balance | told bank | told business | told ca | told called | told card | told case | told charge | told check | told company | told contact | told credit | told customer | told day | told debt | told didnt | told different | told dispute | told dont | told email | told file | told going | told help | told information | told issue | told late | told letter | told loan | told longer | told make | told manager | told money | told month | told need | told needed | told new | told nt | told option | told owe | told owed | told paid | told pay | told payment | told person | told phone | told problem | told receive | told received | told record | told remove | told rep | told representative | told request | told said | told send | told sent | told stop | told submit | told supervisor | told thing | told time | told told | told u | told wait | told want | told wanted | told way | told week | told work | told wrong | told xxxx | told xxxxxxxx | told yes | toll | toll free | tomorrow | ton | tone | tonight | took | took account | took advantage | took day | took information | took loan | took money | took month | took payment | took place | took time | took week | took xxxx | took year | tool | total | total balance | total loan | total loss | total paid | total payment | total xxxx | totaled | totaling | totaling xxxx | totally | touch | tow | town | toyota | toyota financial | trace | track | tracked | tracking | tracking number | trade | trade commission | trade line | traded | tradeline | tradelines | trading | trail | trained | training | trans | trans union | transaction | transaction account | transaction fee | transaction history | transaction posted | transaction xxxx | transaction xxxxxxxx | transcript | transfer | transfer account | transfer fee | transfer fund | transfer money | transfer xxxx | transfered | transferred | transferred account | transferred loan | transferred supervisor | transferred xxxx | transferring | transition | transmission | transparent | transpired | transportation | transunion | transunion credit | transunion report | transunion reporting | transunion xxxx | transworld | travel | traveling | treasury | treat | treated | treated like | treating | treatment | tree | tree servicing | tremendous | tremendously | trial | trial modification | trial payment | trial period | trick | tricked | tried | tried calling | tried contact | tried contacting | tried dispute | tried explain | tried make | tried multiple | tried numerous | tried pay | tried reach | tried resolve | tried time | tried use | tried work | tried xxxx | trip | trouble | truck | true | truly | trust | trust xxxx | trusted | trustee | trustee sale | trustee xxxx | truth | truth lending | try | try collect | try make | try resolve | trying | trying buy | trying collect | trying contact | trying help | trying 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0.0 | 0.0 | 0.0 | 0.0 | 0.157743 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.135627 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.146464 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.132179 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.086438 | 0.0 | 0.088588 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.049133 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.128427 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.044486 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.124302 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.052735 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.071054 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.043281 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.081491 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 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| 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.077341 | 0.073575 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.088803 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.069543 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.109316 | 0.0 | 0.068469 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.078596 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.050216 | 0.0 | 0.0 | 0.0 | 0.059344 | 0.0 | 0.0 | 0.064437 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.075007 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | 0.0 | 0.073337 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.090397 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.058124 | 0.0 | 0.041351 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.049494 | 0.0 | 0.0 | 0.0 | 0.0 | 0.07149 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.070983 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.060337 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.074641 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.10272 | 0.092376 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 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0.0 | 0.0 | 0.12367 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.07721 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.048979 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.077112 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.050973 | 0.0 | 0.0 | 0.088196 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.087118 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.047168 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.061901 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.066291 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.040274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.049913 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.050420 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.074712 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.071999 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.055984 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.114664 | 0.0 | 0.0 | 0.0 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.161321 | 0.0 | 0.0 | 0.0 | 0.150367 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.105796 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.00000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.122261 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.162153 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.047760 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.152051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 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| 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.074257 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.117153 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.085274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 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0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.186702 | 0.0 | 0.0 | 0.0 | 0.0 | 0.00000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.089086 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.147885 | 0.0 | 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0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.073194 | 0.0 | 0.0 | 0.113612 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.098442 | 0.0 | 0.138108 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.144944 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.090629 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.161612 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.095331 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.102977 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.109761 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.143847 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.085932 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
"""
To begin the model building step - we will start with Multinomial Naive Bayes and Logistic Regression as baseline
Next we will move on to build better models
Also, we will use GridSearch and / or RandomSearch for hyperparameter tuning to find best models
Finally we will evaluate the models on the basis of F1 score and also look at the AUC and ROC and PR plots
"""
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
models = [MultinomialNB()
, LogisticRegression(random_state=43)
, DecisionTreeClassifier()
# ,LinearSVC()
]
corpus = df_text['clean_text']
dic_tfv, tfv, text_tfv_reduced = run_TFIDF(corpus,custom_stopwords=False, vocabs = top_n_uniques)
features_reduced = text_tfv_reduced.toarray()
print(features_reduced.shape)
product_groups_y = df_text['product_group'].values
# 5 Cross-validation
CV = 5
cv_df = pd.DataFrame(index=range(CV * len(models)))
entries = []
for model in models:
model_name = model.__class__.__name__
print("fitting %s model ... " %str(model_name))
accuracies = cross_val_score(model, text_tfv_reduced, product_groups_y, scoring='accuracy', cv=CV)
print("%s model has accruacy = " %str(model_name), accuracies)
for fold_idx, accuracy in enumerate(accuracies):
entries.append((model_name, fold_idx, accuracy))
cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'accuracy'])
mean_accuracy = cv_df.groupby('model_name').accuracy.mean()
std_accuracy = cv_df.groupby('model_name').accuracy.std()
acc = pd.concat([mean_accuracy, std_accuracy], axis= 1,
ignore_index=True)
acc.columns = ['Mean Accuracy', 'Standard deviation']
acc
Creating TF-IDF based on MonoGram and BiGram only due to computation limitations... Limiting maximum features to 2541 in TF-IDF only due to computation limitations... fitting TF-IDF ... transforming TF-IDF ... (268343, 2541) fitting MultinomialNB model ... MultinomialNB model has accruacy = [0.81113865 0.81057966 0.80655499 0.81016621 0.81120966] fitting LogisticRegression model ... LogisticRegression model has accruacy = [0.84764016 0.8456092 0.8435596 0.84555042 0.84966833] fitting DecisionTreeClassifier model ... DecisionTreeClassifier model has accruacy = [0.76328979 0.76383014 0.76334569 0.75978237 0.76078855]
| Mean Accuracy | Standard deviation | |
|---|---|---|
| model_name | ||
| DecisionTreeClassifier | 0.762207 | 0.001802 |
| LogisticRegression | 0.846406 | 0.002326 |
| MultinomialNB | 0.809930 | 0.001934 |
plt.figure(figsize=(8,5))
sns.boxplot(x='model_name', y='accuracy',
data=cv_df,
color='lightblue',
showmeans=True)
plt.title("MEAN ACCURACY (cv = 5)", size=14);
# !pip uninstall tensorflow
# !pip install tensorflow
# import nltk
# import tensorflow as tf
# import keras
Creating functions for hyperparameters training </i>
====================================================
do_classify : This function performs classification using the best hyperparameters, which are determined using cv_optimize and then tests the model on the test set. This function prints the training and test accuracy of the classifier and plots the confusion matrix
cv_optimize : This function perform cross-validation using Grid search and is used for hyperparameter tuning and model selection. This function is called in the do_classify function which performs the classification
plot_confusion_matrix : This function plots the confusion matrix
make_pr : This function plots the Precision-Recall curve and also prints the AP(Average Precision)
from sklearn.metrics import average_precision_score
from sklearn.metrics import precision_recall_curve
import itertools
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix',
cmap=plt.cm.Blues):
"""
Parameters
------------------------
cm : The confusion matrix to be plotted
normalization : A boolean value for normalizing the values in confusion matrix
title : Title of the plot
cmap : The color map to be used
Returns
-------------------------
Plots the confusion matrix.
PS-This function in called in do_classify.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
print(cm)
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
###############################################################################################
def make_pr(name, clf, ytest, xtest, ax=None):
"""
Parameters
------------------------
name : Name of the classifier in strings
clf : The tuned classifier
ytest : The true responses in the test set
xtest : Features of the test set
ax : The number of PR curves to print
Returns
-------------------------
ax: Plots the PR curve along with AP value.
PS: Code taken from http://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html
"""
average_precision = average_precision_score(ytest, clf.predict_proba(xtest)[:,1])
precision, recall, _ = precision_recall_curve(y_test, clf.predict_proba(xtest)[:,1])
plt.step(recall, precision, color='b', alpha=0.2,
where='post')
plt.fill_between(recall, precision, step='post', alpha=0.2,
color='b')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall curve: AP={0:0.2f}'.format(
average_precision))
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import f1_score, make_scorer
def cv_optimize(clf, parameters, X, y, n_jobs=-1, n_folds=5):
"""
Parameters
------------------------
clf : The classifier
parameters : A dictionary of parameters on which grid search is to be performed
X : Dataframe of features
y : A Dataframe of responses/target
n_jobs : Parameter for number of cores to engage when performing cv
n_folds : The number of cv folds
Returns
-------------------------
best : The best hyperparameters for the classifier
"""
skf = StratifiedKFold(n_splits=n_folds, shuffle = True, random_state = 999)
gs = GridSearchCV(clf, param_grid=parameters, cv=skf.split(X,y), n_jobs=n_jobs,
scoring=f1)
gs.fit(X, y)
print ("BEST", gs.best_params_, gs.best_score_, gs.cv_results_)
best = gs.best_estimator_
return best
#######################################################################################
def rv_optimize(clf, parameters, X, y, n_jobs=-1, n_folds=5):
"""
Parameters
------------------------
clf : The classifier
parameters : A dictionary of parameters on which grid search is to be performed
X : Dataframe of features
y : A Dataframe of responses/target
n_jobs : Parameter for number of cores to engage when performing cv
n_folds : The number of cv folds
Returns
-------------------------
best : The best hyperparameters for the classifier
"""
skf = StratifiedKFold(n_splits=n_folds, shuffle = True, random_state = 999)
rs = RandomizedSearchCV(clf, param_distributions=parameters, cv=skf.split(X,y), n_jobs=n_jobs,
scoring=f1)
rs.fit(X, y)
print ("BEST", gs.best_params_, gs.best_score_, gs.cv_results_)
best = gs.best_estimator_
return best
#######################################################################################
def do_classify(clf, parameters, Xtrain,Xtest,ytrain,ytest, n_folds=5,
n_jobs=-1):
"""
Parameters
------------------------
clf : The classifier
parameters : A dictionary of parameters on which grid search is to be performed
Xtrain : Train Dataframe of features
Xtest : Test Dataframe of features
ytrain : Train Dataframe of responses/target
ytest : Test Dataframe of responses/target
n_jobs : Parameter for number of cores to engage when performing cv
n_folds : The number of cv folds
Returns
-------------------------
clf : The classifier with optimized hyperparameters
Xtrain : The Train Dataframe of features
Xtest : The Test Dataframe of features
ytrain : The Train Dataframe of responses/target
ytest : The Test Dataframe of responses/target
Prints the training and test accuracy.
Plots the confusion matrix.
"""
#performing grid search to find best hyperparameters
f1 = make_scorer(f1_score,average='weighted')
if parameters:
clf = cv_optimize(clf, parameters, Xtrain, ytrain, n_jobs=n_jobs, n_folds=n_folds)
# Fitting the tuned classifier
clf=clf.fit(Xtrain, ytrain)
# Calculating the training accuracy
training_accuracy = clf.score(Xtrain, ytrain)
# Calculating the test accuracy
test_accuracy = clf.score(Xtest, ytest)
print ("############# based on standard predict ################")
print ("Accuracy on training data: %0.2f" % (training_accuracy))
print ("Accuracy on test data: %0.2f" % (test_accuracy))
#Calculating the confusion matrix
cnf_matrix = confusion_matrix(ytest, clf.predict(Xtest))
np.set_printoptions(precision=3)
plt.figure()
#Plotting the confusion matrix
plot_confusion_matrix(cnf_matrix, classes=[0,1],
title='Confusion matrix, without normalization')
plt.show()
print ("########################################################")
return clf, Xtrain, ytrain, Xtest, ytest
def tune_classifier(clf, parameters, Xtrain,Xtest,ytrain,ytest, n_folds=5,
n_jobs=-1, tuning='GridSearch'):
"""
Parameters
------------------------
clf : The classifier
parameters : A dictionary of parameters on which grid search is to be performed
Xtrain : Train Dataframe of features
Xtest : Test Dataframe of features
ytrain : Train Dataframe of responses/target
ytest : Test Dataframe of responses/target
n_jobs : Parameter for number of cores to engage when performing cv
n_folds : The number of cv folds
Returns
-------------------------
clf : The classifier with optimized hyperparameters
Xtrain : The Train Dataframe of features
Xtest : The Test Dataframe of features
ytrain : The Train Dataframe of responses/target
ytest : The Test Dataframe of responses/target
Prints the training and test accuracy.
Plots the confusion matrix.
"""
#performing grid search to find best hyperparameters
f1 = make_scorer(f1_score,average='weighted')
if parameters:
if tuning=='GridSearch':
clf = cv_optimize(clf, parameters, Xtrain, ytrain, n_jobs=n_jobs, n_folds=n_folds)
elif tuning=='RandomSearch':
clf = rv_optimize(clf, parameters, Xtrain, ytrain, n_jobs=n_jobs, n_folds=n_folds)
# Fitting the tuned classifier
clf=clf.fit(Xtrain, ytrain)
# Calculating the training accuracy
training_accuracy = clf.score(Xtrain, ytrain)
# Calculating the test accuracy
test_accuracy = clf.score(Xtest, ytest)
print ("############# based on standard predict ################")
print ("Accuracy on training data: %0.2f" % (training_accuracy))
print ("Accuracy on test data: %0.2f" % (test_accuracy))
#Calculating the confusion matrix
cnf_matrix = confusion_matrix(ytest, clf.predict(Xtest))
np.set_printoptions(precision=3)
plt.figure()
#Plotting the confusion matrix
plot_confusion_matrix(cnf_matrix, classes=[0,1],
title='Confusion matrix, without normalization')
plt.show()
print ("########################################################")
return clf, Xtrain, ytrain, Xtest, ytest
vectorizer = TfidfVectorizer(min_df=3, max_features=10000,
strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}',
ngram_range=(1, 1), use_idf=1,smooth_idf=1,sublinear_tf=1,
stop_words = 'english', vocabulary = top_n_uniques)
corpus = df_text['clean_text']
X = vectorizer.fit_transform(corpus)
Y = df_text['product_group'].values
x_train, x_valid, y_train, y_valid = train_test_split(X, Y, random_state=43, test_size=0.2)
#Creating a f1 scoring function
f1 = make_scorer(f1_score,average='weighted')
from sklearn.naive_bayes import MultinomialNB
parameters = None
tune_classifier(MultinomialNB(),parameters, x_train, x_valid ,y_train,y_valid,n_folds=5,
n_jobs=-1)
############# based on standard predict ################ Accuracy on training data: 0.77 Accuracy on test data: 0.76 Confusion matrix, without normalization [[ 1687 703 865 341 31 0 437] [ 47 3377 1881 529 80 0 155] [ 6 80 14923 728 131 0 182] [ 9 56 2218 9488 164 0 192] [ 16 84 1231 978 3664 0 334] [ 176 238 226 169 7 39 91] [ 11 9 474 179 10 0 7423]]
########################################################
(MultinomialNB(),
<214674x122962 sparse matrix of type '<class 'numpy.float64'>'
with 12563571 stored elements in Compressed Sparse Row format>,
array(['mortgage', 'mortgage', 'debt_collection', ..., 'credit_card',
'mortgage', 'debt_collection'], dtype=object),
<53669x122962 sparse matrix of type '<class 'numpy.float64'>'
with 3161710 stored elements in Compressed Sparse Row format>,
array(['loan', 'bank_service', 'mortgage', ..., 'credit_reporting',
'debt_collection', 'mortgage'], dtype=object))
from sklearn.linear_model import LogisticRegression
parameters = {"C": [0.01, 0.1, 1, 10, 100, 1000],
"penalty":['l2','l1']}
tune_classifier(LogisticRegression(),parameters, x_train, x_valid ,y_train,y_valid,n_folds=5,
n_jobs=-1)
BEST {'C': 1, 'penalty': 'l2'} 0.8444761056143738 {'mean_fit_time': array([1.580e+02, 3.210e-01, 3.711e+02, 1.423e+00, 6.196e+02, 7.423e-01,
2.930e+02, 1.535e+00, 1.920e+02, 3.293e+00, 1.136e+02, 1.199e+00]), 'std_fit_time': array([4.996e+00, 1.951e-01, 2.034e+02, 8.383e-01, 1.092e+01, 3.772e-01,
1.516e+02, 1.530e+00, 2.953e+01, 1.483e+00, 4.913e+00, 4.364e-01]), 'mean_score_time': array([0.881, 0. , 1.158, 0. , 1.199, 0. , 1.509, 0. , 1.134,
0. , 0.589, 0. ]), 'std_score_time': array([0.167, 0. , 0.149, 0. , 0.11 , 0. , 0.355, 0. , 0.361,
0. , 0.078, 0. ]), 'param_C': masked_array(data=[0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100, 100, 1000,
1000],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object), 'param_penalty': masked_array(data=['l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2',
'l1', 'l2', 'l1'],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False],
fill_value='?',
dtype=object), 'params': [{'C': 0.01, 'penalty': 'l2'}, {'C': 0.01, 'penalty': 'l1'}, {'C': 0.1, 'penalty': 'l2'}, {'C': 0.1, 'penalty': 'l1'}, {'C': 1, 'penalty': 'l2'}, {'C': 1, 'penalty': 'l1'}, {'C': 10, 'penalty': 'l2'}, {'C': 10, 'penalty': 'l1'}, {'C': 100, 'penalty': 'l2'}, {'C': 100, 'penalty': 'l1'}, {'C': 1000, 'penalty': 'l2'}, {'C': 1000, 'penalty': 'l1'}], 'split0_test_score': array([0.786, nan, 0.835, nan, 0.846, nan, 0.842, nan, 0.842,
nan, 0.839, nan]), 'split1_test_score': array([0.784, nan, 0.831, nan, 0.843, nan, 0.841, nan, 0.839,
nan, 0.839, nan]), 'split2_test_score': array([0.787, nan, 0.834, nan, 0.844, nan, 0.841, nan, 0.838,
nan, 0.839, nan]), 'split3_test_score': array([0.786, nan, 0.832, nan, 0.844, nan, 0.845, nan, 0.842,
nan, 0.841, nan]), 'split4_test_score': array([0.785, nan, 0.833, nan, 0.844, nan, 0.84 , nan, 0.839,
nan, 0.839, nan]), 'mean_test_score': array([0.786, nan, 0.833, nan, 0.844, nan, 0.842, nan, 0.84 ,
nan, 0.839, nan]), 'std_test_score': array([0.001, nan, 0.001, nan, 0.001, nan, 0.002, nan, 0.001,
nan, 0.001, nan]), 'rank_test_score': array([ 6, 7, 5, 8, 1, 9, 2, 10, 3, 11, 4, 12], dtype=int32)}
############# based on standard predict ################
Accuracy on training data: 0.86
Accuracy on test data: 0.85
Confusion matrix, without normalization
[[ 3365 278 108 94 44 86 89]
[ 294 4808 496 328 80 30 33]
[ 71 370 13969 1024 399 1 216]
[ 71 210 1090 10175 411 8 162]
[ 91 128 468 565 4932 4 119]
[ 169 46 16 21 16 668 10]
[ 71 33 223 170 103 0 7506]]
########################################################
(LogisticRegression(C=1),
<214674x122962 sparse matrix of type '<class 'numpy.float64'>'
with 12563571 stored elements in Compressed Sparse Row format>,
array(['mortgage', 'mortgage', 'debt_collection', ..., 'credit_card',
'mortgage', 'debt_collection'], dtype=object),
<53669x122962 sparse matrix of type '<class 'numpy.float64'>'
with 3161710 stored elements in Compressed Sparse Row format>,
array(['loan', 'bank_service', 'mortgage', ..., 'credit_reporting',
'debt_collection', 'mortgage'], dtype=object))
from sklearn.ensemble import RandomForestClassifier
parameters = {"n_estimators": [50,100,150,200], "max_depth":range(2,20,1)}
do_classify(RandomForestClassifier(),parameters, x_train, x_valid ,y_train,y_valid,n_folds=5,
n_jobs=-1)
BEST {'max_depth': 19, 'n_estimators': 200} 0.5530530118331888 {'mean_fit_time': array([ 6.78 , 11.772, 17.439, 23.273, 9.266, 16.855,
26.36 , 36.162, 12.443, 24.289, 41.292, 51.646,
16.408, 31.738, 43.858, 59.268, 17.811, 36.306,
49.478, 68.451, 20.375, 41.313, 61.258, 81.742,
24.377, 48.592, 69.413, 94.223, 25.847, 51.222,
77.689, 105.218, 31.363, 57.833, 91.828, 115.377,
33.997, 623.312, 1020.861, 122.175, 34.873, 66.463,
100.381, 140.171, 38.544, 72.452, 116.578, 162.438,
47.231, 92.324, 127.117, 173.423, 46.025, 95.147,
135.644, 194.358, 51.419, 103.588, 161.516, 240.824,
61.408, 136.774, 202.583, 273.74 , 69.707, 141.759,
208.645, 275.704, 76.065, 153.447, 227.8 , 249.694]), 'std_fit_time': array([2.632e-01, 1.699e-01, 8.264e-01, 1.182e+00, 8.289e-01, 4.710e-01,
1.547e+00, 3.207e+00, 9.131e-01, 1.676e+00, 3.347e+00, 3.451e+00,
1.049e+00, 2.542e+00, 1.817e+00, 5.442e+00, 6.012e-01, 2.883e+00,
1.746e+00, 6.214e+00, 6.875e-01, 2.494e+00, 4.555e+00, 4.970e+00,
2.530e+00, 3.249e+00, 5.613e+00, 8.089e+00, 6.448e-01, 1.858e+00,
7.400e+00, 6.887e+00, 4.265e+00, 2.256e+00, 6.509e+00, 9.534e+00,
1.902e+00, 4.592e+02, 7.109e+00, 7.022e+00, 3.079e+00, 5.223e+00,
6.020e+00, 1.178e+01, 2.840e+00, 2.972e+00, 9.430e+00, 1.581e+01,
1.940e+00, 6.210e+00, 4.612e+00, 1.438e+01, 1.792e+00, 7.259e+00,
6.281e+00, 1.387e+01, 1.686e+00, 9.302e+00, 9.394e+00, 2.215e+01,
2.408e+00, 4.460e+00, 1.178e+01, 1.955e+01, 1.407e+00, 4.671e+00,
1.798e+01, 1.968e+01, 5.177e+00, 7.842e+00, 1.335e+01, 4.364e+01]), 'mean_score_time': array([1.052, 1.436, 2.004, 2.52 , 1.134, 1.639, 2.211, 2.983, 1.171,
2.274, 2.613, 3.03 , 1.327, 1.945, 2.37 , 3.044, 1.323, 1.723,
2.382, 2.974, 1.241, 1.846, 2.343, 2.978, 1.262, 1.824, 2.367,
2.974, 1.213, 1.851, 2.362, 3.639, 1.392, 1.983, 2.398, 3.144,
1.26 , 1.9 , 2.23 , 2.742, 1.217, 1.777, 2.362, 3.064, 1.302,
1.897, 2.646, 3.492, 1.639, 2.118, 2.667, 3.299, 1.392, 2.055,
2.766, 3.433, 1.406, 2.434, 3.166, 4.18 , 1.683, 2.584, 3.59 ,
4.519, 1.751, 2.894, 3.477, 4.775, 1.882, 2.727, 3.172, 2.565]), 'std_score_time': array([0.034, 0.059, 0.142, 0.086, 0.128, 0.122, 0.139, 0.214, 0.083,
0.214, 0.283, 0.211, 0.034, 0.08 , 0.062, 0.121, 0.181, 0.066,
0.136, 0.222, 0.072, 0.058, 0.104, 0.22 , 0.083, 0.093, 0.154,
0.084, 0.074, 0.121, 0.068, 0.931, 0.117, 0.11 , 0.071, 0.189,
0.04 , 0.175, 0.199, 0.093, 0.083, 0.052, 0.076, 0.117, 0.067,
0.108, 0.221, 0.249, 0.248, 0.153, 0.126, 0.058, 0.055, 0.064,
0.045, 0.09 , 0.051, 0.491, 0.492, 0.323, 0.13 , 0.263, 0.378,
0.694, 0.252, 0.551, 0.437, 0.713, 0.113, 0.164, 0.253, 0.592]), 'param_max_depth': masked_array(data=[2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6,
6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10,
10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14,
14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17,
17, 18, 18, 18, 18, 19, 19, 19, 19],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False],
fill_value='?',
dtype=object), 'param_n_estimators': masked_array(data=[50, 100, 150, 200, 50, 100, 150, 200, 50, 100, 150,
200, 50, 100, 150, 200, 50, 100, 150, 200, 50, 100,
150, 200, 50, 100, 150, 200, 50, 100, 150, 200, 50,
100, 150, 200, 50, 100, 150, 200, 50, 100, 150, 200,
50, 100, 150, 200, 50, 100, 150, 200, 50, 100, 150,
200, 50, 100, 150, 200, 50, 100, 150, 200, 50, 100,
150, 200, 50, 100, 150, 200],
mask=[False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False,
False, False, False, False, False, False, False, False],
fill_value='?',
dtype=object), 'params': [{'max_depth': 2, 'n_estimators': 50}, {'max_depth': 2, 'n_estimators': 100}, {'max_depth': 2, 'n_estimators': 150}, {'max_depth': 2, 'n_estimators': 200}, {'max_depth': 3, 'n_estimators': 50}, {'max_depth': 3, 'n_estimators': 100}, {'max_depth': 3, 'n_estimators': 150}, {'max_depth': 3, 'n_estimators': 200}, {'max_depth': 4, 'n_estimators': 50}, {'max_depth': 4, 'n_estimators': 100}, {'max_depth': 4, 'n_estimators': 150}, {'max_depth': 4, 'n_estimators': 200}, {'max_depth': 5, 'n_estimators': 50}, {'max_depth': 5, 'n_estimators': 100}, {'max_depth': 5, 'n_estimators': 150}, {'max_depth': 5, 'n_estimators': 200}, {'max_depth': 6, 'n_estimators': 50}, {'max_depth': 6, 'n_estimators': 100}, {'max_depth': 6, 'n_estimators': 150}, {'max_depth': 6, 'n_estimators': 200}, {'max_depth': 7, 'n_estimators': 50}, {'max_depth': 7, 'n_estimators': 100}, {'max_depth': 7, 'n_estimators': 150}, {'max_depth': 7, 'n_estimators': 200}, {'max_depth': 8, 'n_estimators': 50}, {'max_depth': 8, 'n_estimators': 100}, {'max_depth': 8, 'n_estimators': 150}, {'max_depth': 8, 'n_estimators': 200}, {'max_depth': 9, 'n_estimators': 50}, {'max_depth': 9, 'n_estimators': 100}, {'max_depth': 9, 'n_estimators': 150}, {'max_depth': 9, 'n_estimators': 200}, {'max_depth': 10, 'n_estimators': 50}, {'max_depth': 10, 'n_estimators': 100}, {'max_depth': 10, 'n_estimators': 150}, {'max_depth': 10, 'n_estimators': 200}, {'max_depth': 11, 'n_estimators': 50}, {'max_depth': 11, 'n_estimators': 100}, {'max_depth': 11, 'n_estimators': 150}, {'max_depth': 11, 'n_estimators': 200}, {'max_depth': 12, 'n_estimators': 50}, {'max_depth': 12, 'n_estimators': 100}, {'max_depth': 12, 'n_estimators': 150}, {'max_depth': 12, 'n_estimators': 200}, {'max_depth': 13, 'n_estimators': 50}, {'max_depth': 13, 'n_estimators': 100}, {'max_depth': 13, 'n_estimators': 150}, {'max_depth': 13, 'n_estimators': 200}, {'max_depth': 14, 'n_estimators': 50}, {'max_depth': 14, 'n_estimators': 100}, {'max_depth': 14, 'n_estimators': 150}, {'max_depth': 14, 'n_estimators': 200}, {'max_depth': 15, 'n_estimators': 50}, {'max_depth': 15, 'n_estimators': 100}, {'max_depth': 15, 'n_estimators': 150}, {'max_depth': 15, 'n_estimators': 200}, {'max_depth': 16, 'n_estimators': 50}, {'max_depth': 16, 'n_estimators': 100}, {'max_depth': 16, 'n_estimators': 150}, {'max_depth': 16, 'n_estimators': 200}, {'max_depth': 17, 'n_estimators': 50}, {'max_depth': 17, 'n_estimators': 100}, {'max_depth': 17, 'n_estimators': 150}, {'max_depth': 17, 'n_estimators': 200}, {'max_depth': 18, 'n_estimators': 50}, {'max_depth': 18, 'n_estimators': 100}, {'max_depth': 18, 'n_estimators': 150}, {'max_depth': 18, 'n_estimators': 200}, {'max_depth': 19, 'n_estimators': 50}, {'max_depth': 19, 'n_estimators': 100}, {'max_depth': 19, 'n_estimators': 150}, {'max_depth': 19, 'n_estimators': 200}], 'split0_test_score': array([0.142, 0.142, 0.141, 0.142, 0.15 , 0.147, 0.146, 0.148, 0.157,
0.165, 0.161, 0.156, 0.19 , 0.18 , 0.193, 0.182, 0.219, 0.223,
0.206, 0.214, 0.237, 0.241, 0.24 , 0.248, 0.267, 0.26 , 0.269,
0.295, 0.315, 0.322, 0.306, 0.318, 0.325, 0.36 , 0.335, 0.351,
0.404, 0.36 , 0.362, 0.376, 0.413, 0.393, 0.41 , 0.391, 0.439,
0.446, 0.426, 0.433, 0.453, 0.449, 0.447, 0.447, 0.461, 0.469,
0.47 , 0.468, 0.488, 0.496, 0.481, 0.496, 0.51 , 0.524, 0.511,
0.519, 0.536, 0.521, 0.535, 0.535, 0.549, 0.557, 0.546, 0.551]), 'split1_test_score': array([0.142, 0.142, 0.142, 0.142, 0.149, 0.145, 0.145, 0.145, 0.17 ,
0.169, 0.156, 0.164, 0.195, 0.189, 0.177, 0.186, 0.213, 0.213,
0.206, 0.21 , 0.247, 0.264, 0.247, 0.256, 0.29 , 0.279, 0.272,
0.272, 0.328, 0.308, 0.313, 0.31 , 0.306, 0.365, 0.359, 0.343,
0.372, 0.358, 0.376, 0.367, 0.419, 0.389, 0.382, 0.389, 0.448,
0.406, 0.421, 0.427, 0.454, 0.455, 0.439, 0.452, 0.483, 0.467,
0.477, 0.478, 0.48 , 0.493, 0.486, 0.498, 0.502, 0.509, 0.506,
0.503, 0.527, 0.544, 0.534, 0.53 , 0.527, 0.563, 0.54 , 0.549]), 'split2_test_score': array([0.142, 0.142, 0.143, 0.141, 0.145, 0.144, 0.144, 0.144, 0.193,
0.17 , 0.154, 0.164, 0.207, 0.178, 0.195, 0.178, 0.219, 0.212,
0.231, 0.204, 0.25 , 0.24 , 0.24 , 0.243, 0.317, 0.296, 0.301,
0.264, 0.277, 0.312, 0.291, 0.307, 0.366, 0.325, 0.325, 0.321,
0.357, 0.399, 0.373, 0.369, 0.378, 0.406, 0.396, 0.38 , 0.436,
0.433, 0.429, 0.416, 0.463, 0.45 , 0.46 , 0.439, 0.473, 0.477,
0.473, 0.469, 0.493, 0.497, 0.491, 0.501, 0.504, 0.519, 0.51 ,
0.523, 0.521, 0.518, 0.527, 0.53 , 0.542, 0.549, 0.555, 0.559]), 'split3_test_score': array([0.142, 0.142, 0.142, 0.142, 0.146, 0.15 , 0.143, 0.146, 0.172,
0.156, 0.156, 0.156, 0.196, 0.193, 0.185, 0.192, 0.229, 0.224,
0.217, 0.214, 0.232, 0.239, 0.24 , 0.264, 0.276, 0.265, 0.272,
0.274, 0.288, 0.3 , 0.299, 0.326, 0.358, 0.351, 0.334, 0.323,
0.362, 0.35 , 0.363, 0.374, 0.396, 0.386, 0.378, 0.408, 0.426,
0.434, 0.423, 0.416, 0.456, 0.449, 0.462, 0.434, 0.46 , 0.467,
0.472, 0.46 , 0.488, 0.496, 0.486, 0.506, 0.513, 0.499, 0.515,
0.507, 0.537, 0.526, 0.524, 0.536, 0.561, 0.538, 0.54 , 0.554]), 'split4_test_score': array([0.142, 0.142, 0.142, 0.142, 0.159, 0.149, 0.144, 0.146, 0.167,
0.161, 0.163, 0.163, 0.17 , 0.196, 0.169, 0.174, 0.237, 0.262,
0.219, 0.215, 0.243, 0.253, 0.255, 0.237, 0.302, 0.277, 0.281,
0.267, 0.308, 0.311, 0.29 , 0.302, 0.363, 0.347, 0.353, 0.358,
0.389, 0.399, 0.38 , 0.366, 0.376, 0.409, 0.395, 0.418, 0.42 ,
0.421, 0.419, 0.427, 0.441, 0.461, 0.443, 0.439, 0.458, 0.474,
0.493, 0.463, 0.477, 0.498, 0.503, 0.501, 0.5 , 0.514, 0.518,
0.501, 0.51 , 0.552, 0.548, 0.524, 0.539, 0.549, 0.567, 0.552]), 'mean_test_score': array([0.142, 0.142, 0.142, 0.142, 0.15 , 0.147, 0.145, 0.146, 0.172,
0.164, 0.158, 0.161, 0.192, 0.187, 0.184, 0.182, 0.224, 0.227,
0.215, 0.211, 0.242, 0.247, 0.244, 0.25 , 0.291, 0.276, 0.279,
0.274, 0.303, 0.311, 0.3 , 0.312, 0.344, 0.349, 0.341, 0.339,
0.377, 0.373, 0.371, 0.37 , 0.396, 0.397, 0.392, 0.397, 0.434,
0.428, 0.423, 0.424, 0.454, 0.453, 0.45 , 0.442, 0.467, 0.471,
0.477, 0.468, 0.485, 0.496, 0.489, 0.5 , 0.506, 0.513, 0.512,
0.51 , 0.526, 0.532, 0.534, 0.531, 0.543, 0.551, 0.55 , 0.553]), 'std_test_score': array([0. , 0. , 0. , 0. , 0.005, 0.002, 0.001, 0.001, 0.012,
0.005, 0.003, 0.004, 0.012, 0.007, 0.01 , 0.006, 0.009, 0.018,
0.009, 0.004, 0.007, 0.01 , 0.006, 0.009, 0.018, 0.012, 0.012,
0.011, 0.018, 0.007, 0.009, 0.009, 0.024, 0.014, 0.013, 0.015,
0.017, 0.022, 0.007, 0.004, 0.017, 0.009, 0.012, 0.014, 0.01 ,
0.013, 0.004, 0.007, 0.007, 0.005, 0.009, 0.006, 0.01 , 0.004,
0.008, 0.006, 0.006, 0.002, 0.007, 0.003, 0.005, 0.008, 0.004,
0.009, 0.01 , 0.014, 0.008, 0.004, 0.011, 0.008, 0.01 , 0.003]), 'rank_test_score': array([70, 71, 69, 72, 65, 66, 68, 67, 61, 62, 64, 63, 57, 58, 59, 60, 54,
53, 55, 56, 52, 50, 51, 49, 45, 47, 46, 48, 43, 42, 44, 41, 38, 37,
39, 40, 33, 34, 35, 36, 31, 30, 32, 29, 25, 26, 28, 27, 21, 22, 23,
24, 20, 18, 17, 19, 16, 14, 15, 13, 12, 9, 10, 11, 8, 6, 5, 7,
4, 2, 3, 1], dtype=int32)}
############# based on standard predict ################
Accuracy on training data: 0.61
Accuracy on test data: 0.58
Confusion matrix, without normalization
[[ 975 58 2742 108 10 0 171]
[ 36 1101 4534 255 11 0 132]
[ 4 7 15745 161 9 0 124]
[ 3 15 5960 5928 43 0 178]
[ 14 14 3586 611 1654 0 428]
[ 44 11 807 36 3 0 45]
[ 4 0 2279 94 0 0 5729]]
########################################################
(RandomForestClassifier(max_depth=19, n_estimators=200),
<214674x122962 sparse matrix of type '<class 'numpy.float64'>'
with 12563571 stored elements in Compressed Sparse Row format>,
array(['mortgage', 'mortgage', 'debt_collection', ..., 'credit_card',
'mortgage', 'debt_collection'], dtype=object),
<53669x122962 sparse matrix of type '<class 'numpy.float64'>'
with 3161710 stored elements in Compressed Sparse Row format>,
array(['loan', 'bank_service', 'mortgage', ..., 'credit_reporting',
'debt_collection', 'mortgage'], dtype=object))
parameters = None
tune_classifier(GradientBoostingClassifier(),parameters, x_train, x_valid ,y_train,y_valid,n_folds=5,
n_jobs=-1)
############# based on standard predict ################ Accuracy on training data: 0.82 Accuracy on test data: 0.81 Confusion matrix, without normalization [[ 3125 377 143 158 71 109 81] [ 267 4547 665 477 59 29 25] [ 69 362 13548 1296 488 3 284] [ 84 275 1282 9852 419 8 207] [ 95 86 563 694 4767 6 96] [ 163 45 29 54 10 632 13] [ 89 28 249 232 295 1 7212]]
########################################################
(GradientBoostingClassifier(),
<214674x2541 sparse matrix of type '<class 'numpy.float64'>'
with 9090335 stored elements in Compressed Sparse Row format>,
array(['mortgage', 'mortgage', 'debt_collection', ..., 'credit_card',
'mortgage', 'debt_collection'], dtype=object),
<53669x2541 sparse matrix of type '<class 'numpy.float64'>'
with 2288391 stored elements in Compressed Sparse Row format>,
array(['loan', 'bank_service', 'mortgage', ..., 'credit_reporting',
'debt_collection', 'mortgage'], dtype=object))
parameters = {'max_depth' : [3, 5, 8],
'n_estimators' : [50, 100, 200],
'learning_rate':[0.01,0.05,0.1]}
tune_classifier(GradientBoostingClassifier(),parameters, x_train, x_valid ,y_train,y_valid,n_folds=5,
n_jobs=-1,tuning='RandomSearch')
from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, classification_report
def final_model(clf):
## train classifier
model = clf.fit(x_train, y_train)
## test on validation
predicted = model.predict(x_valid)
predicted_prob = model.predict_proba(x_valid)
classes = np.unique(y_valid)
y_valid_array = pd.get_dummies(y_valid, drop_first=False).values
## Accuracy, Precision, Recall
accuracy = accuracy_score(y_valid, predicted)
auc = roc_auc_score(y_valid, predicted_prob, multi_class="ovr")
print("Accuracy:", round(accuracy,2))
print("Auc:", round(auc,2))
print("Detail:")
print(classification_report(y_valid, predicted))
## Plot confusion matrix
cm = confusion_matrix(y_valid, predicted)
fig, ax = plt.subplots()
sns.heatmap(cm, annot=True, fmt='d', ax=ax, cmap=plt.cm.Blues,
cbar=False)
ax.set(xlabel="Pred", ylabel="True", xticklabels=classes,
yticklabels=classes, title="Confusion matrix")
plt.yticks(rotation=0)
fig, ax = plt.subplots(1,2, figsize = (20,5))
## Plot roc
for i in range(len(classes)):
fpr, tpr, thresholds = metrics.roc_curve(y_valid_array[:,i], predicted_prob[:,i])
ax[0].plot(fpr, tpr, lw=3, label='{0} (area={1:0.2f})'.format(classes[i], metrics.auc(fpr, tpr))
)
ax[0].plot([0,1], [0,1], color='navy', lw=3, linestyle='--')
ax[0].set(xlim=[-0.05,1.0], ylim=[0.0,1.05],
xlabel='False Positive Rate', ylabel="True Positive Rate (Recall)",
title="Receiver operating characteristic")
ax[0].legend(loc="lower right")
ax[0].grid(True)
## Plot precision-recall curve
for i in range(len(classes)):
precision, recall, thresholds = metrics.precision_recall_curve(
y_valid_array[:,i], predicted_prob[:,i])
ax[1].plot(recall, precision, lw=3,
label='{0} (area={1:0.2f})'.format(classes[i],
metrics.auc(recall, precision))
)
ax[1].set(xlim=[0.0,1.05], ylim=[0.0,1.05], xlabel='Recall',
ylabel="Precision", title="Precision-Recall curve")
ax[1].legend(loc="best")
ax[1].grid(True)
plt.show()
return model
mnb = final_model(MultinomialNB())
feature_names = vectorizer.get_feature_names()
coefs_mnb = sorted(zip(feature_names, mnb.coef_[0]), key = lambda x: x[1], reverse=True)
fig, axs = plt.subplots(figsize=(20,4))
a = pd.DataFrame(coefs_mnb[:50])
a.set_index(0, inplace = True)
a.plot.bar(ax=axs)
axs.set_title('Top 50 Features for MultiNomial Naive Bayes')
plt.show()
Accuracy: 0.81
Auc: 0.96
Detail:
precision recall f1-score support
bank_service 0.79 0.76 0.78 4064
credit_card 0.76 0.75 0.75 6069
credit_reporting 0.80 0.86 0.83 16050
debt_collection 0.81 0.77 0.79 12127
loan 0.79 0.72 0.75 6307
money_transfers 0.88 0.57 0.69 946
mortgage 0.89 0.92 0.91 8106
accuracy 0.81 53669
macro avg 0.82 0.77 0.79 53669
weighted avg 0.81 0.81 0.81 53669
lr = final_model(LogisticRegression(C=1, penalty='l2'))
feature_names = vectorizer.get_feature_names()
coefs_lr = sorted(zip(feature_names, lr.coef_[0]), key = lambda x: x[1], reverse=True)
fig, axs = plt.subplots(figsize=(20,4))
a = pd.DataFrame(coefs_lr[:50])
a.set_index(0, inplace = True)
a.plot.bar(ax=axs)
axs.set_title('Top 50 Features for Logistic Regression')
plt.show()
Accuracy: 0.85
Auc: 0.98
Detail:
precision recall f1-score support
bank_service 0.82 0.83 0.82 4064
credit_card 0.82 0.80 0.81 6069
credit_reporting 0.86 0.86 0.86 16050
debt_collection 0.83 0.84 0.83 12127
loan 0.82 0.79 0.80 6307
money_transfers 0.83 0.72 0.77 946
mortgage 0.92 0.93 0.93 8106
accuracy 0.85 53669
macro avg 0.84 0.83 0.83 53669
weighted avg 0.85 0.85 0.85 53669
rf = final_model(RandomForestClassifier(n_estimators=200, max_depth=19, random_state=43))
importances = rf.feature_importances_
std = np.std([tree.feature_importances_ for tree in rf.estimators_], axis=0)
forest_importances = pd.Series(importances, index=feature_names)
fig, axs = plt.subplots(figsize=(20,4))
pd.DataFrame(forest_importances).sort_values(by = 0,ascending=False)[:50].plot.bar(ax=axs)
ax.set_title("Feature importances using MDI")
Accuracy: 0.79
Auc: 0.96
Detail:
precision recall f1-score support
bank_service 0.80 0.71 0.76 4064
credit_card 0.82 0.64 0.72 6069
credit_reporting 0.72 0.92 0.81 16050
debt_collection 0.78 0.78 0.78 12127
loan 0.87 0.63 0.73 6307
money_transfers 1.00 0.17 0.30 946
mortgage 0.90 0.90 0.90 8106
accuracy 0.79 53669
macro avg 0.84 0.68 0.71 53669
weighted avg 0.80 0.79 0.78 53669
Text(0.5, 1.0, 'Feature importances using MDI')
final_model(DecisionTreeClassifier())
Accuracy: 0.76
Auc: 0.83
Detail:
precision recall f1-score support
bank_service 0.66 0.66 0.66 4064
credit_card 0.68 0.65 0.67 6069
credit_reporting 0.80 0.85 0.83 16050
debt_collection 0.75 0.74 0.75 12127
loan 0.70 0.66 0.68 6307
money_transfers 0.60 0.58 0.59 946
mortgage 0.85 0.84 0.84 8106
accuracy 0.76 53669
macro avg 0.72 0.71 0.72 53669
weighted avg 0.76 0.76 0.76 53669
"""
Out of all the models tested - Logistic Regression showed the most promising performance
Gradient Boosting model also showed high potential - However, due to limitation of time
GBM model could not be tuned to generate the best performance - this can be addressed in future work
Moreover, more advanced techniques using word-embedding and sequence model such as LSTM NN
can be tested in future. As an alternative, we tested the performance on a basic LSTM model and found
it to be very powerful!
However, based on the performance of the model using Logistic Regression
we are quite comfortable. Moreover, logistic regression model provides better explainability
and overall persimonious. Therefore, Logistic Regression model is the "Chamption Model" for this project
"""
"""
Thank you for taking the time to go over the work and I appreciate your time and considerations
"""